Lead Machine Learning Engineer

As a Lead Machine Learning Engineer, you will be leading the engineering of agentic AI systems and partnering with Data Scientists, Data Engineers, Data Analysts, DevSecOps, and other professionals helping to implement traditional models and agentic AI systems to deliver stability, producibility, scalability, and integration with other products and services. You will also architect AI capabilities that combine large language models with semantic knowledge layers, knowledge graphs, ontologies, and deterministic reasoning to support accurate, explainable, and governable business decisions. You will implement capabilities to solve sophisticated business problems, deploy innovative products, services, and experiences to delight our customers! In addition to advanced technical expertise and experience, you will bring excellent problem-solving, communication, and teamwork skills, along with agile ways of working, strong business insights, an inclusive leadership attitude, and a continuous learning focus on all that you do.

Here is what you can expect in a typical day:

• Architect, Design and, develop, and deploy ML models and Agentic AI systems that solve real-world business problems, while working in collaboration with the Product and Data Science teams; remove complex technical impediments
• Design and implement semantic knowledge layers that represent domain entities, relationships, taxonomies, ontologies, and business rules for use by LLM and agentic applications.
• Build knowledge graph and Graph RAG capabilities that combine vector retrieval with entity resolution, graph traversal, multi-hop reasoning, and relationship-aware context retrieval.
• Solve complex problems by writing and testing application code, developing and validating ML models and Agentic AI, and automating tests and deployment using LLM tools like Claude Code.
• Leverage cloud-based architectures and technologies to deliver optimized ML models, semantic reasoning, and Agentic systems at scale.
• Construct optimized data, ingestion, knowledge graph, semantic layer, and memory pipelines to feed ML models and Agentic systems
• Define approaches for ontology lifecycle management, schema evolution, temporal knowledge, data lineage, graph freshness, and synchronization with source systems.?
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Bring a strong understanding of relevant and emerging technologies, provide input and coach team members, and embed learning and innovation in the day-to-day
• Work on complex problems in which analysis of situations or data requires an evaluation of intangible variables.
• Use programming languages including but not limited to Python, C++, SQL, Cypher, Gremlin, or other graph query languages.

• Bachelor of Computer Science or Engineering or experience in related fields
• Ability to coach others with minimal guidance and effectively leverage diverse ideas, experiences, thoughts, and perspectives to the benefit of the organization
• Experience with agile development methodologies and Specification-Driven Development (SDD)
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company’s business
• Ability to learn new skills and knowledge on an ongoing basis through self-initiative and tackling challenges
• Excellent problem-solving, communication, and collaboration skills

Advanced experience and/or expertise with several of the following: Software Engineering & System Design: Requirement analysis, coding, and testing, version control, microservices architecture, building RESTful APIs, distributed computing, architecture patterns, general understanding of computer architecture, Object-oriented programming concepts Machine Learning and Deep Learning: Good understanding of ML algorithms like linear regression, logistic regression, etc.; supervised, unsupervised, and reinforcement learning; AI Frameworks like TensorFlow, PyTorch, scikit-learn, etc., Neural network, NLP, computer vision; and predictive analytics. Agentic and Generative AI: Architecture, development, evaluation, and deployment of LLM and multi-agent systems - including orchestration, tool use, context engineering, memory, session state, guardrails, human-in-the-loop controls, and agent observability.?Experience combining LLMs with knowledge graphs, ontologies, semantic retrieval, and deterministic?rules?or constraint engines to reduce unsupported reasoning and improve consistency. Knowledge Graphs & Ontologies: Experience building semantic layers using knowledge graphs, ontologies, and entity relationship models; integrating them with LLMs and Agentic AI to enable multi-hop reasoning, explainability, deterministic business rules, and governance. Model Performance and Governance: model monitoring, model validation, bias detection, explainability, performance, drift, outliers, and agent observability, etc. Model Deployment: Thorough Understanding of ADLC (Agent Development Life Cycle), CI/CD/CT pipelines (using tools like GitHub Actions, Jenkins, etc.), A/B testing. Pipeline frameworks like SageMaker pipeline, etc.; model and data versioning. Data Integration, Transformation & Processing: Transforming and mapping raw data to generate insights. Data wrangling through various tools. Understanding big data ecosystems, relational, NOSQL, and graph databases, unstructured and semi-structured data. Data processing on distributed systems with Spark/PySpark Knowledge of how databases are structured and function efficiently. May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc. SQL (relational), Unstructured (NoSQL), Graph/ontology (Graph DB), and semantic data models. Statistics and Computing: Strong knowledge of Linear Algebra, Probability and Statistics, Multivariate Calculus, and distributions like Poisson, Normal, Binomial, etc. Programming Languages: Python, C++, SQL, Cypher, Gremlin, or other graph query languages