AI/ML Engineers

Role Overview: You will be responsible for designing, developing, and deploying scalable Machine Learning and Generative AI solutions. Your expertise in data engineering, model lifecycle management, and modern AI frameworks will be crucial in focusing on production-grade AI/ML systems and automation through MLOps and LangOps practices. Key Responsibilities: - Design, train, and optimize machine learning models for real-world enterprise applications. - Build and maintain end-to-end ML pipelines, including data pre-processing, feature engineering, model training, validation, and deployment. - Collaborate with data engineers and software developers to integrate ML models into production systems. - Monitor and maintain model performance, detect data drifts, and implement retraining strategies for continuous improvement. Generative AI (GenAI) Responsibilities: - Architect LLM-powered applications with dynamic intent routing and tool integration. - Develop agentic workflows using frameworks such as LangGraph or equivalents to ensure determinism, scalability, and guardrails. - Integrate MCP-compatible tools and services to extend GenAI capabilities. - Build and optimize RAG (Retrieval-Augmented Generation) systems using advanced embedding, chunking, and reranking strategies. - Enhance search efficiency through vector store optimization, hybrid search, and metadata schema design. - Design robust prompting frameworks and function-calling templates. - Evaluate generic vs. fine-tuned LLMs based on cost, latency, accuracy, and maintainability. - Implement NL2SQL and guarded SQL execution patterns. - Connect models with microservices and enterprise APIs for secure, real-time data access. - Define and maintain data schemas, metadata, and lineage for reliability and traceability. - Establish evaluation datasets and automated regression tests for RAG and agentic solutions. - Monitor performance metrics - precision, recall, hallucination rate, latency, and cost. - Enforce data privacy, PII protection, and access control across deployments. - Manage versioning of prompts, models, and embeddings, and oversee controlled rollouts with A/B testing. - Implement monitoring, tracing, and telemetry for AI agents and tool invocations. - Define fallback, timeout, and retry policies to ensure system reliability and resiliency. Qualifications Required: - Strong proficiency in Python (NumPy, Pandas, Scikit-learn). - Hands-on experience with TensorFlow or PyTorch. - Experience with supervised, unsupervised, and reinforcement learning methods. - Proficient with SQL/NoSQL databases, data preprocessing, and feature engineering. - Exposure to CI/CD, pipeline automation, and model deployment workflows.,