Deployment Engineer – GenAI with Python & Azure OpenAI, Hybrid Pune

 

Forward Deployment Engineer – GenAI/Agentic AI at Futran Solutions | Pune (Hybrid)

Futran Solutions is hiring a Forward Deployment Engineer to lead the design, development, and deployment of enterprise-grade GenAI and Agentic AI applications. This is a mid-to-senior role ideal for experienced Python developers with deep expertise in Large Language Models, Retrieval-Augmented Generation (RAG), and production-ready AI system architecture. With 8–10 years of experience, you’ll architect scalable AI solutions, integrate enterprise APIs, optimize performance, and work across LLMOps pipelines. If you’re proficient in LangChain, LangGraph, CrewAI, Azure OpenAI, vector databases, FastAPI, Docker, Kubernetes, and CI/CD, this role offers the chance to shape cutting-edge AI infrastructure in a hybrid Pune environment. Apply now and join our free WhatsApp and Telegram channels for instant daily job alerts.

Table of Contents

• Job Details
• About Futran Solutions
• Job Description
• Eligibility Criteria
• Key Responsibilities
• Required Skills
• Who Can Apply
• How to Prepare
• Frequently Asked Questions
• How to Apply
• Stay Updated — Free Job Alerts
• Disclaimer

Job Details

Field	Value
Company	Futran Solutions
Job Title	Forward Deployment Engineer – GenAI/Agentic AI
Experience Required	8–10 Years
Qualification	As per company standards
Salary / CTC	As per company standards
Location	Pune, India
Work Model	Hybrid
Job Type	Full-Time
Contact	[email protected]

About Futran Solutions

Futran Solutions is a forward-thinking technology solutions company focused on building enterprise-grade AI and generative AI applications that solve real-world business challenges. The organization specializes in deploying advanced AI systems, agentic AI frameworks, and machine learning infrastructure for enterprise clients. With a team of experienced engineers, Futran Solutions prioritizes production-ready systems, scalability, and seamless enterprise integration. The company is actively expanding its AI engineering team in Pune, recognizing the growing demand for specialists who can bridge the gap between cutting-edge AI research and reliable, deployable enterprise solutions. Futran’s hybrid work model reflects its commitment to flexibility and attracting talent across India.

Job Description

The Forward Deployment Engineer role is centered on architecting, building, and deploying enterprise-scale GenAI and Agentic AI applications. You will work at the intersection of artificial intelligence research, software engineering, and systems architecture, translating innovative AI concepts into production-ready systems that serve enterprise clients. This is not a research-only or development-only position—it demands end-to-end ownership from design through optimization, with emphasis on reliability, performance, and maintainability.

Your day-to-day work will involve designing AI solution architectures using modern frameworks like LangChain and LangGraph, implementing Retrieval-Augmented Generation (RAG) pipelines to enhance LLM capabilities, orchestrating multi-agent workflows with CrewAI, and integrating these systems with enterprise APIs and data sources. You’ll leverage Azure OpenAI for LLM capabilities, vector databases for semantic search and memory, and FastAPI for building scalable backend services. Your infrastructure expertise in Docker, Kubernetes, and CI/CD will ensure smooth deployment and scaling of AI applications in production environments.

Beyond coding, this role demands strong communication and collaboration skills to work with cross-functional teams, articulate complex AI concepts to non-technical stakeholders, and provide technical guidance to junior engineers. You’ll own MLOps and LLMOps practices—monitoring model performance, managing data pipelines, versioning code and models, and continuously optimizing system behavior. The ideal candidate balances deep technical prowess with pragmatism: you can write production-grade Python code, architect microservices, and also understand the business value of AI investments.

Eligibility Criteria

• Experience (Mandatory): 8–10 years of professional software development and AI/ML engineering experience.
• Programming Language (Mandatory): Expert-level proficiency in Python; demonstrated ability to write clean, testable, production-ready code.
• Generative AI & LLM Knowledge (Mandatory): Solid hands-on experience with Large Language Models, prompt engineering, and fine-tuning; familiarity with LLM concepts like context windows, token limits, and response optimization.
• RAG & AI Frameworks (Mandatory): Proven experience implementing Retrieval-Augmented Generation systems, working with LangChain and LangGraph for chain orchestration, and CrewAI for multi-agent systems.
• Cloud & LLM Platforms (Mandatory): Hands-on experience with Azure OpenAI or similar cloud LLM providers; understanding of API integration and model deployment.
• Vector Databases & Search (Mandatory): Practical knowledge of vector databases (e.g., Pinecone, Weaviate, Milvus, Chroma) and semantic search implementations.
• Backend & Containerization (Mandatory): Experience building APIs with FastAPI or similar frameworks; solid Docker and Kubernetes knowledge for containerization and orchestration.
• DevOps & MLOps (Mandatory): Strong understanding of CI/CD pipelines, MLOps/LLMOps practices, model versioning, monitoring, and production deployment strategies.
• Communication (Mandatory): Strong verbal and written communication skills to explain AI concepts, document systems, and collaborate across teams.
• Preferred: Prior experience with enterprise API integrations, microservices architecture, and Agile development practices.

Key Responsibilities

• AI Solution Architecture: Design end-to-end GenAI and Agentic AI application architectures aligned with enterprise requirements, scalability constraints, and security standards.
• LLM Integration & Optimization: Integrate Large Language Models from Azure OpenAI or similar platforms into enterprise applications; optimize prompts, manage context, and tune response quality.
• RAG Pipeline Development: Build and optimize Retrieval-Augmented Generation systems using vector databases and semantic search to enhance LLM accuracy and reduce hallucinations.
• Multi-Agent System Design: Architect and implement multi-agent AI systems using frameworks like CrewAI, enabling complex workflows and task orchestration across autonomous agents.
• FastAPI & Backend Services: Develop scalable, production-grade REST APIs and backend services using FastAPI to expose AI models and integrate with enterprise systems.
• Containerization & Orchestration: Deploy AI applications using Docker and Kubernetes, ensuring seamless scaling, load balancing, and high availability across production environments.
• MLOps & LLMOps Implementation: Establish monitoring, logging, and observability practices for AI models; manage model versioning, performance tracking, and continuous improvement cycles.
• Enterprise API Integration: Integrate AI applications with existing enterprise APIs, databases, and data pipelines; ensure secure and efficient data flow.
• Performance Optimization: Identify and resolve performance bottlenecks, optimize inference latency, and manage costs related to LLM API calls and compute resources.
• Collaboration & Documentation: Work closely with product managers, data engineers, and stakeholders to understand requirements; maintain comprehensive technical documentation and knowledge sharing.

Required Skills

• Python (Expert): Deep expertise in Python for backend development, data processing, and AI/ML applications; familiarity with async frameworks and performance optimization.
• Large Language Models (LLMs): Hands-on experience working with models from OpenAI, Azure OpenAI, or similar providers; understanding of prompt engineering, token management, and model behavior tuning.
• Retrieval-Augmented Generation (RAG): Practical knowledge of RAG architecture, vector embeddings, semantic search, and how RAG mitigates LLM hallucinations in enterprise contexts.
• LangChain: Production experience building AI chains and workflows using LangChain; comfort with chain composition, memory management, and tool integration.
• LangGraph: Hands-on experience with LangGraph for state management and complex workflow orchestration in multi-step AI applications.
• CrewAI: Experience designing and deploying multi-agent AI systems using CrewAI; understanding of agent roles, tasks, and inter-agent communication.
• Azure OpenAI / Cloud LLM Platforms: Proficiency in Azure OpenAI API, model deployment, and integration; familiarity with Azure services like Azure Cognitive Search.
• Vector Databases: Practical knowledge of at least one vector database (Pinecone, Weaviate, Milvus, Chroma, etc.); understanding of indexing, querying, and scaling considerations.
• FastAPI: Strong experience building RESTful APIs with FastAPI; knowledge of async programming, request validation, and API documentation.
• Docker & Containerization: Proficiency in Docker; ability to build container images, manage dependencies, and write production-ready Dockerfiles.
• Kubernetes: Hands-on Kubernetes experience for deploying, scaling, and managing containerized applications in production environments.
• CI/CD Pipelines: Experience with continuous integration and deployment; familiarity with tools like GitHub Actions, Jenkins, or GitLab CI for automated testing and deployment.
• MLOps / LLMOps: Knowledge of MLOps practices: model versioning (e.g., with DVC or Model Registry), monitoring, A/B testing, and performance tracking for ML and AI systems.
• Communication & Collaboration: Excellent written and verbal communication; ability to explain complex AI concepts to non-technical stakeholders and mentor junior engineers.

Who Can Apply

This role is designed for mid-to-senior software engineers and AI specialists with 8–10 years of proven experience in software development and machine learning. You should have a strong foundation in Python, cloud platforms, containerization, and system design, with recent, hands-on experience in generative AI, LLMs, and enterprise application development. This is not an entry-level or fresher-friendly position; it requires substantial professional depth and demonstrated expertise across the full stack of AI application development.

• Ideal Profile: Senior full-stack AI engineer or ML systems engineer transitioning into GenAI and Agentic AI roles.
• Career Stage: Mid-career (8–10 years) professionals seeking leadership in cutting-edge AI deployment and architecture.
• Domain Fit: Best suited for engineers with proven experience in LLM integrations, production ML systems, and enterprise software deployment.
• Fresher Eligibility: Freshers and early-career professionals (0–3 years) should not apply unless your profile is exceptionally strong in GenAI and LLM experience through projects or internships; this role mandates 8–10 years of professional experience.
• Location Flexibility: Based in or open to relocating to Pune; the hybrid model allows flexibility with occasional office presence.

How to Prepare

• Master LLM Fundamentals: Deep dive into how modern LLMs work—attention mechanisms, tokenization, context windows, and inference optimization. Read research papers on transformer models and study the Azure OpenAI documentation thoroughly.
• Build RAG Projects: Create 2–3 end-to-end RAG projects using LangChain and a vector database (e.g., Pinecone or Weaviate). Include document ingestion, semantic search, and integration with an LLM. Document your project architecture and results.
• LangChain & LangGraph Hands-On: Build complex workflows and chains using LangChain; explore LangGraph for state management in multi-step agent systems. Publish example code on GitHub with clear explanations.
• Multi-Agent Systems with CrewAI: Design and implement at least one multi-agent AI system using CrewAI. Experiment with agent roles, task delegation, and communication patterns between agents.
• FastAPI Backend Development: Build production-grade REST APIs using FastAPI for AI model serving. Include request validation, error handling, async operations, and comprehensive API documentation using Swagger.
• Docker & Kubernetes Mastery: Write Dockerfiles for AI applications; deploy containerized apps to Kubernetes clusters. Practice service scaling, rolling updates, and resource management on minikube or cloud Kubernetes services (e.g., Azure Kubernetes Service).
• CI/CD Pipeline Design: Set up end-to-end CI/CD pipelines for AI applications using GitHub Actions or GitLab CI. Include automated testing, Docker image building, and deployment to Kubernetes.
• MLOps & Model Monitoring: Implement model versioning, tracking, and monitoring. Use tools like MLflow or similar to version models, track experiments, and monitor performance metrics in production.
• Azure OpenAI Integration: Get hands-on with Azure OpenAI services. Integrate Azure OpenAI into a web application; experiment with different models, fine-tuning, and cost optimization strategies.
• System Design for Scale: Practice designing AI systems for enterprise scale: think about latency, throughput, fault tolerance, and cost. Be ready to discuss trade-offs in architecture decisions during interviews.
• Communication & Documentation: Write technical blogs or whitepapers explaining RAG, LLMOps, or multi-agent systems. Practice explaining complex AI concepts in plain language for non-technical audiences.
• Enterprise Integration Patterns: Study how to integrate AI systems with legacy enterprise APIs, databases, and data warehouses. Familiarize yourself with security, authentication, and data governance in enterprise AI deployments.

Frequently Asked Questions

What exactly does a Forward Deployment Engineer do at Futran Solutions?

A Forward Deployment Engineer designs, builds, and deploys enterprise-grade GenAI and Agentic AI applications. You architect AI solutions end-to-end, from integrating LLMs and building RAG pipelines to containerizing and scaling systems in production using Kubernetes. You own both the AI layer and the infrastructure layer, ensuring production-ready, high-performance systems.

Is this role suitable for freshers or early-career professionals?

No. This is explicitly an 8–10 years experience role, making it mid-to-senior level. Freshers and professionals with less than 8 years of experience are not eligible. However, if you have exceptionally strong GenAI and LLM project experience (through internships, side projects, or research), you could reach out to clarify, but it is unlikely the company will consider you.

What is the minimum experience required?

You must have 8–10 years of professional software development and AI/ML engineering experience. This typically includes roles as a senior software engineer, ML engineer, or AI systems engineer with substantial hands-on depth.

Do I need to have worked with Azure OpenAI specifically?

The role specifies Azure OpenAI, but hands-on experience with OpenAI, Anthropic Claude, or similar LLM platforms is likely acceptable. However, having working knowledge of Azure services and Azure OpenAI APIs will strengthen your profile. You can learn Azure-specific details quickly if you have solid LLM integration experience elsewhere.

What if I have never worked with LangChain or LangGraph before?

These are mandatory skills. If you haven’t worked with them, you should start learning immediately. Both are open-source frameworks with excellent documentation. Build 2–3 projects with LangChain before applying to demonstrate proficiency. LangGraph is newer; mastery of LangChain is more critical.

Is Kubernetes experience absolutely required?

Yes. The job description lists Kubernetes as mandatory. You should have hands-on Kubernetes experience deploying and managing containerized applications. If you have only theoretical knowledge, practice on minikube or use free Kubernetes trials from cloud providers (Azure, AWS, GCP) before applying.

What vector database should I learn?

The role does not specify a particular vector database. Pinecone, Weaviate, Milvus, and Chroma are all popular. We recommend learning at least one deeply (e.g., Pinecone or Weaviate) and understanding the architectural differences between them. Experience with multiple databases is a plus.

How important is CrewAI experience?

CrewAI is listed as a mandatory skill, so hands-on experience building multi-agent systems with it is important. If you haven’t used CrewAI, start building projects with it immediately. This is an emerging framework in the agentic AI space, and the role emphasizes agentic workflows.

Will the company train me on these technologies if I lack some skills?

The job description does not mention training. Given the 8–10 years experience requirement and the depth of skills needed, the company likely expects you to come in with most, if not all, of these skills ready to apply day one. Training may happen on company-specific systems or workflows, but not on core technologies like LangChain, Kubernetes, or Python.

What salary should I expect?

The job posting does not disclose a salary range. Compensation is typically determined by your exact experience, skill depth, past employment, and negotiation. For an 8–10 years AI engineer role in Pune at a growth-stage company, market rates are typically in the range of 25–45 LPA for mid-to-senior levels, but verify with the company during conversations. Always ask about salary early in the process.

Is this a Pune-only role, or can I work from home?

The role is Hybrid, meaning you will split time between home and the Pune office. The exact ratio (e.g., 2 days in office, 3 days remote) is not specified in the job posting. Clarify the exact hybrid expectations with the recruiter before applying.

Can I apply if I am based outside Pune?

The role is Hybrid and based in Pune. If you are willing to relocate or visit Pune for the hybrid schedule, you can apply. Remote-only work is unlikely given the hybrid designation. Confirm relocation expectations during the initial conversation with the recruiter.

What is the application deadline?

No specific deadline is mentioned in the job posting. Apply as soon as possible; positions at this level typically fill quickly. Contact [email protected] to confirm if there is a closing date.

How do I apply, and what documents should I prepare?

Email [email protected] with your resume, a cover letter highlighting your GenAI and agentic AI experience, links to GitHub projects or deployed AI systems, and a brief summary of your most relevant work. Be specific about your LLM, RAG, and Kubernetes projects.

What should I highlight in my resume for this role?

Emphasize 3–5 concrete GenAI/LLM projects with clear outcomes: e.g., “Built RAG system using LangChain + Pinecone achieving 95% relevance on enterprise documents” or “Deployed multi-agent AI system with CrewAI orchestrating 6 specialized agents.” Include numbers: latency improvements, cost savings, scalability achieved. Highlight production deployments, not just side projects.

Is there a walk-in or online assessment?

The job posting does not mention a walk-in, online assessment, or coding test. You will likely go through a standard hiring process: resume review, technical interview (discussion of your past projects and system design), coding or architecture discussions, and final rounds with senior leadership. Clarify the process when you reach out to the recruiter.

What is the career growth path after this role?

The job posting does not detail career progression. At Futran Solutions, an 8–10 years engineer in this Forward Deployment Engineer role may progress toward Principal Engineer, AI Architect, or Engineering Manager roles. Discuss career growth expectations during your conversations with the hiring team.

Why is this role called “Forward Deployment Engineer”?

“Forward Deployment” likely refers to the role’s focus on pushing cutting-edge AI technologies (GenAI, agentic AI) into production and enterprise environments quickly and reliably. It emphasizes both innovation and pragmatic, field-ready deployment rather than research-only work.

Should I apply even if I do not have ALL the listed skills?

The job lists mandatory skills (Python, LLMs, RAG, LangChain, LangGraph, CrewAI, Azure OpenAI, vector databases, FastAPI, Docker, Kubernetes, CI/CD, MLOps/LLMOps). If you are missing 2–3 non-critical ones, you might still apply and explain your willingness to learn quickly. However, missing 5+ skills or core ones like Python, Kubernetes, or LLM knowledge significantly reduces your chances. Be honest in your application.

How to Apply

To apply for this Forward Deployment Engineer role at Futran Solutions, prepare a strong application showcasing your GenAI and agentic AI expertise, production deployment experience, and technical depth in Python, LangChain, Kubernetes, and related technologies.

1. Prepare Your Application Materials: Gather your resume (highlighting AI projects, system design, and 8–10 years experience), a cover letter emphasizing GenAI and agentic AI work, GitHub links to relevant projects, and any published blogs or technical articles on LLM systems or MLOps.
2. Tailor Your Resume: Rewrite your resume to emphasize 3–4 concrete GenAI, RAG, or multi-agent AI projects with measurable outcomes. Include specific technologies you have used: LangChain, CrewAI, Azure OpenAI, vector databases, FastAPI, Kubernetes, and CI/CD tools.
3. Compose Your Cover Letter: Write a brief, personalized cover letter explaining why you are interested in Futran Solutions, what excites you about agentic AI, and how your past work aligns with the Forward Deployment Engineer role.
4. Send Your Application: Email your complete application package to the contact provided, using the subject line: Forward Deployment Engineer – GenAI/Agentic AI Application [Your Name]. Include the reference name Nikhil Jathar if provided to you.
5. Follow Up: If you do not receive a response within 7–10 days, send a polite follow-up email reiterating your interest and confirming receipt of your application.
6. Prepare for Technical Discussions: Be ready to discuss your past GenAI projects in detail, explain architectural decisions, walk through code, and discuss system design trade-offs related to AI application deployment.

Email: [email protected]

Reference: Nikhil Jathar

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Disclaimer

This article is for informational purposes only. Job details, including role description, eligibility, experience requirements, and application process, are sourced from the official job posting provided by Futran Solutions and may be subject to change at the company’s discretion. Always verify the role, eligibility criteria, application deadline, and all other details on the official Futran Solutions careers page or by contacting the recruiter directly before submitting your application. itreferraljobs.in does not charge any fee for job referrals, applications, or career guidance and is not affiliated with Futran Solutions or any recruiting agency. We recommend conducting your own research about the company and role before applying.