Data & AI Scientist

Lead the design and delivery of enterprise-scale AI/ML solutions—including LLM/GenAI features—with emphasis on reliability, security, and regulatory compliance. Architect and implement advanced ML systems; build MLOps pipelines and model lifecycle management with MLflow; develop RAG workflows, embeddings, and vector indexes; expose models via secure microservices (FastAPI) with RBAC/ABAC and audit logging. Drive governance and security controls across solutions, mentor junior engineers, and collaborate with cross-functional teams to operationalise AI safely. Requires strong Python and software engineering discipline; hands-on Docker/Kubernetes and Git-based CI/CD; experience with cloud AI stacks (Azure ML or GCP Vertex AI), artefact registries, and secrets management. Knowledge of LLM fundamentals (prompting, embeddings, RAG, guardrails), MLflow/Kubeflow, Airflow/Composer, feature stores (e.g., Feast), vector databases (PGVector/Weaviate/Pinecone), LangChain/LlamaIndex, observability tools (Prometheus/Grafana/OpenTelemetry), and evaluation frameworks (Evidently, TruLens). Also includes secure engineering practices (tokenisation/masking, KMS/Key Vault, policy-as-code). Based in Hyderabad, India with hybrid working options.