AI/ML Engineer

Design, train, and optimize machine learning models for real-world applications.
Build end-to-end ML pipelines including data preprocessing, feature engineering, model training, validation, and deployment.
Collaborate with data engineers and software developers to integrate ML models into production systems.
Monitor model performance, detect data drifts, and retrain models for continuous improvement.
GenAI Agentic Solution design and orchestration.
Architect LLM-powered applications, including intent routing across tools/skills.
Implement agentic workflows using frameworks such as LangGraph or equivalents; decompose tasks, manage tool invocation, and ensure determinism/guardrails.
Integrate MCP-compatible tools and services to extend system capabilities.
Build effective RAG systems: chunking strategies, embedding model selection, vector indexing, reranking, and grounding to authoritative data.
Optimize vector stores and search (ANN, hybrid, filters, metadata schemas).
Develop robust prompting patterns and templates; structure prompts for tool use and function calling.
Compare generic vs fine-tuned LLMs for intent routing; make data-driven choices on cost, latency, accuracy, and maintainability.
Implement NL2SQL (and guarded SQL execution) patterns; connect to microservices and enterprise systems via secure APIs.
Define and enforce data schemas, metadata, and lineage for reliable retrieval.
Establish evaluation datasets and automated regressions for RAG and agents.
Monitor quality (precision/recall, hallucination rate), latency, cost, and safety.
Apply guardrails, PII handling, access controls, and policy enforcement end-to-end.
Version prompts, models, embeddings, and pipelines; manage A/B tests and rollout.
Instrument tracing/telemetry for agent steps and tool calls; implement fallback/timeout/retry policies.