Lead AI/ML Engineer | About YouAs a Lead AI/ML Engineer, you will be responsible for designing, building, and scaling enterprise-grade AI solutions that power intelligent decision-making and business outcomes. You will lead the development of production AI systems, including retrieval-augmented generation (RAG), model serving, AI orchestration, evaluation frameworks, and governance controls. Working closely with data engineering, platform engineering, and product teams, you will ensure AI solutions are accurate, explainable, secure, and compliant while delivering measurable business value. The ideal candidate combines deep AI/ML expertise with strong software engineering fundamentals and experience deploying production-grade generative AI solutions in regulated or high-stakes environments.
Lead AI/ML Engineer | Day-to-Day- Design, build, and optimize enterprise AI solutions, including retrieval-augmented generation (RAG), semantic retrieval, model orchestration, and AI-powered decision support capabilities.
- Develop and maintain AI model serving infrastructure, embedding services, vector search platforms, and inference pipelines to support scalable production workloads.
- Build AI evaluation frameworks and testing methodologies to measure model accuracy, groundedness, response quality, reliability, and business performance.
- Implement AI governance, security, and guardrail controls, including PII protection, prompt injection defenses, access controls, model monitoring, and compliance requirements.
- Partner with data engineering teams to integrate governed data sources, semantic layers, and structured-query frameworks that ensure trustworthy and explainable AI outputs.
- Collaborate with product, platform, security, and business stakeholders to define AI product capabilities, optimize performance, and continuously improve AI solution effectiveness.
Lead AI/ML Engineer | Skills & Experience- 6+ years of software engineering experience, including 2+ years building and deploying production AI, Machine Learning, NLP, LLM, or Retrieval-Augmented Generation (RAG) solutions.
- Strong expertise in Python and modern AI/ML frameworks, with experience developing scalable, production-grade AI applications and services.
- Hands-on experience with LLM technologies, including model serving, vector databases, embeddings, prompt engineering, retrieval pipelines, agent/tool orchestration, and AI workflow automation.
- Experience implementing AI governance and security controls, including model evaluation, guardrails, PII protection, prompt injection prevention, access controls, and responsible AI practices.
- Proven ability to collaborate across data, platform, product, and engineering teams, delivering reliable, explainable, and business-aligned AI solutions in complex enterprise environments.
- Preferred: Experience with self-hosted/open-weight models, vLLM, Ollama, Qdrant, pgvector, LlamaIndex, Haystack, semantic layers, financial services, or other highly regulated industries.
