Senior Software Engineering Lead
Position Summary
We are seeking a highly experienced Senior Software Engineering Lead to drive the design, development, and deployment of advanced artificial intelligence solutions across the organization.
Key Responsibilities
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.
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.
Establish standards for model performance monitoring, data drift detection, cost optimization, and production reliability.
Oversee cloud-based AI workloads on AWS, GCP, or Azure, including GPU and TPU infrastructure where applicable.
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
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.