Position Summary
We are seeking a highly experienced Senior AI Engineering Lead to drive the design, development, and deployment of advanced artificial intelligence solutions across the organization. This role will serve as both a senior technical leader and people manager, responsible for leading AI strategy execution, building production-ready AI systems, and guiding a team of AI and software developers.
The Senior AI Engineering Lead will be responsible for developing custom AI solutions that go beyond basic LLM API integration. This includes predictive modeling, Retrieval-Augmented Generation or RAG systems, agentic AI workflows, fine-tuned models, multimodal AI solutions, and scalable AI platforms that deliver measurable business impact.
This position requires strong hands-on technical expertise, leadership maturity, strategic thinking, and the ability to translate complex business needs into reliable, secure, and production-grade AI solutions.
Key Responsibilities
AI Strategy and Technical Leadership
- Lead the research, design, and implementation of AI and machine learning solutions aligned with business priorities.
- Provide technical direction for AI architecture, model selection, AI infrastructure, and production deployment.
- Evaluate emerging AI technologies, foundation models, agentic frameworks, and infrastructure tools to determine suitability for company use.
- Define technical standards, best practices, and governance for AI development, deployment, monitoring, and responsible use.
- Partner with product, engineering, data, and business leaders to identify high-value AI opportunities and translate them into executable roadmaps.
AI Development and Solutions Engineering
- Design and build custom AI products, including predictive models, RAG pipelines, agentic AI workflows, semantic search systems, and fine-tuned models.
- Develop AI-powered features and services that integrate with existing platforms, workflows, and business systems.
- Build and optimize LLM-powered applications using frameworks such as LangChain, LangGraph, LlamaIndex, OpenAI SDK, Anthropic SDK, and Model Context Protocol or MCP.
- Design embedding pipelines, vector database structures, hybrid search, re-ranking, and graph-augmented retrieval solutions.
- Lead development of multimodal AI solutions involving text, image, audio, structured data, and other domain-specific inputs.
Production AI and MLOps
- Lead deployment, monitoring, versioning, and continuous improvement of AI models in production environments.
- Build and maintain MLOps pipelines for model training, evaluation, deployment, retraining, and lifecycle management.
- Ensure AI systems are scalable, reliable, secure, explainable, and aligned with business and compliance requirements.
- Oversee cloud-based AI workloads on AWS, GCP, or Azure, including GPU and TPU infrastructure where applicable.
- Establish standards for model performance monitoring, data drift detection, cost optimization, and production reliability.
People Management and Team Leadership
- Directly manage AI and software developers, including hiring support, onboarding, performance management, mentoring, workload prioritization, and career development.
- Provide coaching and technical guidance to junior, mid-level, and senior developers.
- Promote a culture of technical excellence, accountability, continuous learning, innovation, and responsible AI development.
- Review technical output, ensure code quality, support architecture decisions, and remove blockers for the team.
- Work closely with leadership to align team capacity, priorities, and deliverables with company objectives.
Required Experience
Minimum Requirement
Software Development 5+ years AI / ML Engineering in Production 3+ years LLM / Generative AI Development 2+ years Cloud AI Deployment 2+ years MLOps / Model Lifecycle Management 2+ years People Management / Technical Leadership 2+ years
Required Technical Skills
- Strong proficiency in Python and working knowledge of TypeScript or JavaScript for AI system integration.
- Strong experience with PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.
- Hands-on experience with RAG architecture, embeddings, semantic search, vector databases, hybrid retrieval, re-ranking, and graph-augmented retrieval.
- Experience with LangChain, LangGraph, LlamaIndex, OpenAI SDK, Anthropic SDK, and Model Context Protocol or MCP.
- Experience with vector databases such as Pinecone, Weaviate, pgvector, ChromaDB, or Qdrant.
- Experience with fine-tuning techniques such as LoRA, QLoRA, RLHF, and DPO.
- Strong understanding of MLOps, CI/CD, Docker, Kubernetes, model serving, monitoring, and model versioning.
- Experience with cloud platforms such as AWS, GCP, or Azure, including scalable AI and ML workloads.
- Strong knowledge of databases, data pipelines, SQL, NoSQL, preprocessing, testing, Git, code review, and software engineering best practices.
Leadership Competencies
- Strong strategic thinking and ability to translate business goals into AI solutions.
- Ability to lead technical teams, manage priorities, and deliver results in a fast-moving environment.
- Strong communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.
- High accountability, sound judgment, resilience, curiosity, and commitment to continuous improvement.
- Ability to evaluate trade-offs involving performance, cost, risk, scalability, compliance, and user impact.
Nice to Have
- Experience with agentic AI frameworks such as AutoGen, CrewAI, OpenAI Agents SDK, Anthropic Claude Agent SDK, and MCP server development.
- Background in NLP, computer vision, multimodal AI, or applied AI product development.
- Published research, open-source contributions, patents, or strong AI project portfolio.
- Experience with A/B testing, model monitoring, data drift detection, responsible AI, AI safety, model explainability, SHAP, LIME, and AI governance frameworks.
Education
- Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, Engineering, or a related field is preferred.
- Equivalent practical experience will also be considered.
- Relevant certifications such as AWS Machine Learning Specialty, Google Professional Machine Learning Engineer, DeepLearning.AI certifications, or similar AI/ML credentials are a plus.
