Sr. Tech Lead /Engineering Manager

AI Lead / AI Architect AI Lead / AI Architect with a minimum of 7 years of experience in AI/ML, data science, software engineering, or related fields, including strong hands-on experience designing and delivering enterprise AI/ML solutions. Will provide technical leadership for AI initiatives, define AI architecture and solution patterns, and guide AI/ML solutions from business requirements through production deployment. Key Responsibilities • Define the AI/ML architecture and technical strategy for enterprise AI initiatives. • Translate business objectives and use cases into scalable, secure, and production- ready AI solutions. • Lead the design of end-to-end AI architectures, including models, data, APIs, applications, infrastructure, and integration components. • Evaluate AI/ML technologies, frameworks, platforms, and models and recommend the most appropriate solutions. • Establish reusable AI architecture patterns, standards, and best practices. • Define technical approaches for traditional ML, Generative AI, LLMs, RAG, and AI Agents. • Work closely with Business Analysts and Product teams to assess AI use cases, technical feasibility, value, data requirements, and dependencies. • Guide Data Scientists and AI/ML Engineers in model development, experimentation, evaluation, and productionization. • Work with Data Engineers to define data architecture and pipelines required for AI/ML solutions. • Work with Backend and Frontend teams to integrate AI capabilities into enterprise applications. • Work with DevOps/MLOps teams to establish production deployment architectures across on-premises Kubernetes and cloud environments. • Define requirements for model serving, inference, scalability, monitoring, and lifecycle management. • Establish appropriate approaches for model versioning, deployment, monitoring, retraining, and rollback. • Ensure AI solutions meet requirements for security, performance, scalability, availability, and maintainability. • Identify technical risks, architectural constraints, dependencies, and technology gaps. • Lead technical design reviews and provide guidance on complex AI/ML architecture decisions. • Ensure AI solutions are aligned with enterprise architecture and security standards. • Provide technical leadership and mentorship to AI/ML and engineering teams. • Stay current with developments in Generative AI, LLMs, AI Agents, MLOps, and AI engineering and assess their relevance to the organization. Generative AI & LLM • Design architectures for LLM-powered applications and enterprise Generative AI solutions. • Define appropriate approaches for: o RAG o Embeddings o Vector databases o Prompt engineering o LLM orchestration o AI Agents o Tool/function calling o Model routing and selection • Evaluate commercial and open-source models based on performance, cost, latency, security, and business requirements. • Design solutions that support multiple LLM providers or models where appropriate. • Establish approaches for LLM evaluation, monitoring, and quality measurement. • Address AI-specific concerns such as hallucination, data leakage, prompt injection, access control, and model reliability. • Guide teams in selecting appropriate frameworks such as LangGraph, LangChain, or similar AI application frameworks. AI/ML Architecture • Design end-to-end architectures covering: Data → ML/AI Model → Model Serving → API → Application → Monitoring • Define appropriate model development and production environments. • Establish patterns for real-time and batch inference. • Define integration patterns for AI/ML models with enterprise applications and APIs. • Ensure AI solutions can operate across on-premises, cloud, and hybrid environments. • Design for scalability and high availability of AI/ML workloads. • Define appropriate compute requirements, including CPU/GPU resources where applicable. • Establish architecture patterns for AI/ML observability and monitoring. MLOps & Productionization • Define the MLOps architecture and lifecycle for AI/ML solutions. • Work with DevOps/MLOps teams to implement automated ML/AI deployment pipelines. • Establish standards for: o Model versioning o Model registry o Experiment tracking o Model deployment o Model monitoring o Model rollback o Model retraining • Guide the deployment of models to Kubernetes-based environments. • Ensure AI/ML solutions can be deployed consistently across development, testing, and production. • Define requirements for model performance monitoring and detection of data/model drift. • Support the transition of prototypes and proof-of-concepts into production-grade solutions. AI Governance & Responsible AI • Define technical controls for AI security, privacy, governance, and responsible AI. • Establish appropriate human oversight for AI-powered decisions. • Ensure appropriate protection of sensitive data used by AI/ML systems. • Define access controls for AI models, APIs, data sources, and AI platforms. • Establish requirements for AI/ML logging, traceability, monitoring, and auditability. • Support evaluation of AI risks, limitations, and potential unintended behavior. Required Skills & Experience • 7+ years of professional experience in AI/ML, Data Science, Software Engineering, or related technical fields. • Strong experience designing and delivering production AI/ML solutions. • Strong understanding of machine learning, deep learning, and AI architectures. • Strong experience with Python and modern AI/ML frameworks. • Strong understanding of APIs, microservices, databases, and distributed systems. • Hands-on experience with Generative AI and LLM technologies. • Strong understanding of RAG, embeddings, vector databases, and AI Agents. • Experience designing MLOps and model deployment architectures. • Experience with Kubernetes, Docker, CI/CD, and cloud platforms. • Experience designing solutions for on-premises and cloud environments. • Strong understanding of AI/ML security, scalability, performance, and observability. • Strong communication and technical leadership skills. • Experience working in Agile/Scrum environments and using Jira. Preferred Technologies Experience with some of the following is preferred: • Python • PyTorch / TensorFlow / Scikit-learn • LangGraph / LangChain • MLflow • Kubeflow • KServe • NVIDIA Triton Inference Server • Hugging Face • Vector databases such as Pinecone, Qdrant, Weaviate, Milvus, or similar • Docker / Kubernetes / Helm • Terraform • Azure / AWS / GCP • Azure OpenAI / OpenAI APIs / AWS Bedrock / Google Vertex AI • Kafka or other event-streaming platforms • Prometheus / Grafana and other observability platforms Industry Experience – Preferred Experience delivering AI/ML solutions in Oil & Gas, Energy, Utilities, or other industrial environments is highly desirable. Experience with the following is a plus: • Predictive maintenance • Anomaly detection • Asset optimization • Industrial IoT • Time-series data • Operational analytics • Equipment monitoring • Enterprise data platforms Collaboration • Provide technical direction to Data Scientists, AI/ML Engineers, Data Engineers, Backend Developers, Frontend Developers, DevOps/MLOps, QA, Network/Security, and Business Analysts. • Work with Business Analysts and Product teams to translate business problems into AI/ML solutions. • Work with Data Engineering teams to define data requirements and architecture. • Work with DevOps/MLOps teams to establish production deployment and operational requirements. • Participate in architecture reviews, technical workshops, sprint planning, backlog refinement, and other Agile ceremonies. • Track AI architecture activities, technical risks, dependencies, and deliverables using Jira. • Communicate complex AI/ML architecture concepts clearly to both technical and non-technical stakeholders. Ideal Candidate A senior AI Architect and technical leader who can bridge the gap between business use cases, AI/ML models, data, software engineering, and production infrastructure. The ideal candidate should be able to design end-to-end AI solutions, guide engineering teams, and ensure AI/ML capabilities move from experimentation to secure, scalable, production-ready enterprise solutions across on-premises and cloud environments.