Roles & Responsibilities :

- Architect and lead the development of multi-agent AI systems using frameworks such as LangGraph, CrewAI, and AutoGen enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale.

- Design and operationalize multimodal generative AI pipelines that unify text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini) for rich, cross-modal intelligence.

- Build production-grade RAG and Graph-RAG systems integrating vector databases (Pinecone, pgvector, OpenSearch) and knowledge graphs (Neo4j, AWS Neptune) for semantic retrieval, entity-aware reasoning, and grounded generation.

- Lead LLM fine-tuning, prompt engineering, and model alignment strategies including RLHF, PEFT, LoRA, and instruction tuning to adapt foundation models for specialized enterprise use cases.

- Establish robust LLMOps and MLOps pipelines on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery.

- Develop high-performance Python backend services for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput Gen AI operations.

- Engineer state, memory, and context management subsystems that enable agents to reason temporally, maintain session continuity, manage long-context windows, and coordinate across tools and modalities.

- Implement Responsible AI and AI governance practices including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards ensuring transparency and fairness of deployed models.

- Apply traditional ML and statistical modeling (regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems.

- Continuously research, evaluate, and productionize advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models benchmarking against enterprise-scale performance and safety requirements.

All About You:

- Master's or Bachelor's degree in Computer Science, AI/ML, or Engineering, with significant hands-on experience leading and delivering complex Gen AI or ML engineering programs in production environments.

- Expert-level, hands-on experience designing, building, and deploying large language model (LLM) applications, agentic systems, and RAG pipelines from prototype to production.

- Deep proficiency with LLM ecosystems: OpenAI, Anthropic, Gemini, Hugging Face, LangChain/LangGraph, and open-source foundation models (LLaMA, Mistral, Falcon, etc.).

- Strong command of Gen AI engineering patterns: prompt engineering, chain-of-thought reasoning, tool/function calling, vector embeddings, semantic search, and agent memory architectures.

- Solid applied knowledge of ML fundamentals predictive modeling, deep learning (PyTorch, TensorFlow), and statistical techniques used in tandem with Gen AI for hybrid, interpretable systems.

- Excellent Python engineering skills including async programming, API development (FastAPI), and building inference-ready microservices; SQL proficiency required.

- Hands-on experience with cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI) and familiarity with MLOps/LLMOps tooling (MLflow, Weights & Biases, etc.).

- Strong analytical, communication, and stakeholder management skills with the ability to translate complex Gen AI concepts into business value and lead cross-functional teams toward delivery.