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Design, build, train, fine-tune, evaluate, and deploy Machine Learning, Deep Learning, and Generative AI models into production environments.
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Develop and implement LLM-powered applications using OpenAI, Claude, Llama, Gemini, and other foundation models.
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Build RAG pipelines using LangChain, LangGraph, LlamaIndex, vector databases, and embedding models for enterprise knowledge retrieval.
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Design and implement Agentic AI and multi-agent systems with autonomous planning, tool orchestration, memory, and workflow automation.
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Develop scalable feature engineering pipelines, ML pipelines, feature stores, model serving, and inference APIs.
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Build and maintain MLOps/LLMOps pipelines for model training, versioning, deployment, monitoring, drift detection, and automated retraining.
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Collaborate with cross-functional teams to integrate AI capabilities into customer-facing applications, APIs, and enterprise platforms.
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Translate complex business requirements into scalable AI/ML solutions with measurable business outcomes.
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Optimize AI models for performance, latency, scalability, security, and cost efficiency.
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Implement responsible AI practices including model evaluation, guardrails, governance, explainability, and monitoring.
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Strong programming experience in Python and SQL.
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Hands-on experience with Machine Learning, Deep Learning, NLP, Computer Vision, and predictive analytics.
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Strong experience with PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, and Hugging Face Transformers.
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Expertise in Generative AI, LLMs, Prompt Engineering, RAG, AI Agents, MCP, Function Calling, and Multi-Agent Architectures.
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Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar AI orchestration frameworks.
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Hands-on experience with Vector Databases such as Pinecone, FAISS, ChromaDB, Weaviate, or PGVector.
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Experience deploying AI workloads on AWS (SageMaker, Bedrock, Lambda, ECS, EKS, S3), Azure AI, or Google Vertex AI.
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Experience with MLflow, Kubeflow, SageMaker Pipelines, Model Registry, CI/CD, and model monitoring.
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Knowledge of Docker, Kubernetes, REST APIs, Git, Terraform, and cloud-native architectures.
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