- About Job/Role

Generative AI Platform Engineer / Dev Lead responsible for designing, developing, and deploying AI-powered/GenAI applications and solutions.
Expertise in large language models, search technologies, AI frameworks, and developing AI Agents.
Reporting Manager: Sandeep Kutwal, Sr. Manager
Job Location: Pune

- Roles and Responsibilities

Build, fine-tune, and deploy production-scale LLM applications including AI agents, multimodal systems, and conversational AI with robust API integration, async programming, and enterprise-scale deployment.
Drive GenAI agentic innovation by designing autonomous agents, multi-agent workflows, and tool integrations (LangChain, LangGraph, CrewAI, AutoGen, Hugging Face, NLP, RAG, OpenAI, Claude).
Develop E2E AI solutions and implement design patterns: analyze, design, deliver & support.
Translate business needs into AI-driven solutions.
Design and implement RAG pipelines, embeddings, and vector databases (Pinecone, Milvus, FAISS, Weaviate, VectorDB).
Apply expertise in AI design patterns, ethics, data privacy, security, and governance frameworks.
Work hands-on with AWS & Azure cloud AI/ML services and cloud migration.
Ensure ML/AIOps pipelines are scalable, cost-optimized, and secure.
Mentor and guide GenAI developers on best practices, technology selection, performance optimization, and integration patterns.
Maintain a creative, adaptable, and innovative mindset to apply GenAI and agents to new business use cases.

- Required Qualification

Bachelor’s degree in Computer Science, Engineering, or related field.
Strong technical expertise in AI/ML development, cloud AI services, and enterprise-scale deployments.

- Experience

Overall 7+ years of experience, including 3–5+ years of proven experience in ML/AI and recent experience delivering GenAI solutions and AI Agents.

- Skills

Hands-on experience in Python, .NET/C#, PLSQL.
Proficiency with ML frameworks and data libraries (Pandas, NumPy).
Knowledge of large language models, multimodal AI systems, and conversational AI.
Experience in RAG pipelines, vector databases, and embeddings.
Familiarity with cloud platforms (AWS, Azure), ML/AIOps pipelines, scalability, and cloud security.
Strong analytical, problem-solving, and mentoring abilities.
Creative, adaptable, and capable of applying GenAI to business use cases.

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