Description

• Embed with client SMEs, product owners, business users, and engineers to map workflows, users, data, constraints, and decision points.
• Agree on the first useful release, measurable success criteria, and a practical roadmap from prototype to production.
• Profile unfamiliar client data using Python, pandas, PySpark, and SQL to assess coverage, quality, volumes, distributions, edge cases, and feasibility.
• Build thin working solutions using real client data and APIs, validate them with users, and iteratively harden them for production.
• Design, develop, and deploy enterprise AI and data applications using Python, PySpark, SQL, REST APIs, FastAPI, and cloud data platforms.
• Build Claude-powered applications, copilots, and AI workflows using prompt engineering, RAG, vector databases, semantic search, and agentic AI patterns.
• Design batch, real-time, and streaming data pipelines and integrations across enterprise systems.
• Implement MLOps/LLMOps capabilities, including lifecycle management, CI/CD, MLflow, monitoring, deployment automation, rollback, and production support.
• Create evaluation datasets with client SMEs, measure model and LLM quality, perform structured error analysis, and prioritize improvements.
• Resolve data-access, API, identity, network, security, infrastructure, and deployment blockers with client teams.
• Own post-go-live monitoring, incident diagnosis, rollback, stabilization, latency, performance, reliability, and cost optimization.
• Measure adoption and business impact using agreed KPIs, then complete structured handover with runbooks, support ownership, and knowledge transfer.


What you bring
• 10+ years of experience in data engineering, AI application development, cloud data platforms, and production software engineering.
• Strong hands-on expertise in Python, PySpark, SQL, REST APIs, FastAPI, Git, Docker, Kubernetes, Airflow, MLflow, CI/CD, and distributed processing.
• Experience with Claude, prompt engineering, RAG, vector databases, semantic search, and agentic AI patterns.
• Strong knowledge of ETL/ELT, batch and streaming pipelines, data quality, metadata, lineage, observability, and DataOps.
• Experience operationalizing MLOps/LLMOps, including model lifecycle management, automated deployment, monitoring, rollback, and production support.
• Knowledge of enterprise cloud services across compute, storage, integration, orchestration, networking, monitoring, security, and cost optimization.
• Understanding of IAM/RBAC, encryption, secrets management, privacy controls, auditability, compliance, and secure data access.
• Ability to translate business requirements into scalable designs, define non-functional requirements, estimate effort, and drive implementation.
• BFSI experience and strong client-facing skills across discovery, rapid prototyping, solution shaping, workshops, and technical presentations.
• Bachelor's degree in a relevant discipline. Cloud, Kubernetes, AI engineering, MLOps, data engineering, architecture, or security certifications are preferred

Key Skills

Education

Any Gradute