AI/ML Engineer

We are hiring an AI/ML Engineer who combines core machine learning expertise with hands-on Generative AI experience. You will design, build and ship production-grade ML and GenAI applications with measurable business impact, from classical model development and evaluation to RAG pipelines and multi-agent orchestration, and apply the safeguards that keep these systems reliable in production. This role is for someone who has taken models beyond proof of concept.

Key Responsibilities • Apply core ML techniques to business problems: framing the problem, preparing data and features, and training, tuning and validating models. • Design, build and deploy multiple production-grade GenAI applications in Python using agentic frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen or CrewAI. • Own the GenAI lifecycle: prompt engineering, RAG pipeline design, LLM evaluation and fine-tuning. • Architect multi-agent systems covering supervisor and parallel patterns, human-in-the-loop steps and agent memory. • Implement production safeguards, including guardrails, content moderation, and mitigation of prompt injection and data leakage. • Deploy and monitor models and LLM applications in production, with attention to drift, quality and cost. • Apply sound software engineering practice: system design, API development, testing and CI/CD. • Build on loud AI services (Azure OpenAI, AWS Bedrock or GCP Vertex AI) and vector databases • Define and track evaluation metrics so that business impact and quality can be demonstrated and improved over time. • Collaborate with engineers, project leads and client stakeholders and contribute to design reviews and technical documentation.

Required Qualifications • 5–7 years of experience in AI/ML engineering, with strong core ML expertise and hands-on GenAI experience. • Strong foundation in machine learning: supervised and unsupervised learning, statistics, feature engineering, model training, evaluation and tuning, using frameworks such as scikit-learn, PyTorch or TensorFlow. • Proven experience building and shipping multiple production-grade ML and GenAI applications (not just POCs) with demonstrable business impact. • Strong Python skills and hands-on experience with agentic frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen or CrewAI. • Hands-on experience across the GenAI lifecycle: prompt engineering, RAG pipeline design, LLM evaluation and fine-tuning. • Experience with multi-agent orchestration (supervisor, parallel, human-in-the-loop, agent memory). • Experience applying production safeguards: guardrails, content moderation, and mitigation of prompt injection and data leakage. • Strong software engineering fundamentals: system design, APIs and CI/CD. • Working knowledge of at least one cloud AI service (Azure OpenAI, AWS Bedrock or GCP Vertex AI) and one vector database (Pinecone, Weaviate or pgvector). • Strong Communication skills as you will be working directly with the client • Preferred Qualifications • Experience with Databricks. • Experience with LLM MLOps tooling. • Experience building AI for BI solutions using databricks