Key Responsibilities
· experience in AI/ML, Data Science, Machine Learning Engineering, or a related field.
· We are looking for a Senior AI/ML Engineer with strong hands-on experience across Machine Learning, Deep Learning, MLOps, Generative AI, and Agentic AI.
· The candidate will be responsible for designing, developing, deploying, and scaling AI solutions for enterprise use cases.
· Design and develop end-to-end Machine Learning and Deep Learning solutions.
· Build and productionize ML models using appropriate algorithms and frameworks.
· Develop and manage MLOps pipelines covering model training, deployment, monitoring, and lifecycle management.
· Build Generative AI applications using LLMs, prompt engineering, embeddings, RAG, and vector databases.
· Design and implement Agentic AI solutions, including AI agents, tool calling, multi-agent workflows, and orchestration.
· Integrate AI solutions with enterprise APIs, databases, applications, and knowledge sources.
· Deploy scalable AI solutions on AWS, Azure, or Google Cloud Platform.
· Implement AI application monitoring, evaluation, observability, security, and responsible AI practices.
· Collaborate with product, engineering, data, and business teams to translate business problems into AI solutions.
· Mentor junior engineers and contribute to AI architecture and technology strategy.
Skill Requirements
· Programming: Python, SQL
· Machine Learning: Scikit-learn, XGBoost/LightGBM
· Deep Learning: PyTorch / TensorFlow
· GenAI: LLMs, Prompt Engineering, Embeddings, RAG
· Agentic AI: LangGraph, LangChain or equivalent frameworks
· Vector Search: OpenSearch, FAISS, Pinecone or equivalent
· MLOps: MLflow, Docker, CI/CD, model deployment & monitoring
· Cloud: AWS / Azure / Google Cloud Platform
· APIs & Engineering: REST APIs, FastAPI, Git, containerization
· MCP and A2A protocols
· Multi-agent systems
· LLM and RAG evaluation
· AI observability and guardrails
· Kubernetes
· Knowledge Graph / Graph RAG
· Experience with enterprise-scale AI applications
· The ideal candidate should be a hands-on AI engineer who can take an AI use case from problem definition through development, deployment, and production monitoring.
· Strong problem-solving, communication, system-design, and mentoring skills are expected.
