Hiring Senior Manager AI/ML Engineering.

Experience: 11 - 15 Years.

Must have HR domain experience.

Job Description:

Minimum Qualifications:

- Bachelor's degree or higher in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a related field.

- 15+ years of technology experience with progressive technical and delivery leadership responsibilities.

- 10+ years of experience across AI, machine learning, natural language processing, data science, or data-intensive software engineering.

- Proven experience designing and deploying production Generative AI, retrieval-augmented generation, or Agentic AI solutions at enterprise scale.

- Experience leading cross-functional and globally distributed engineering teams delivering complex enterprise platforms or products.

- Demonstrated hands-on architecture and engineering depth, including design review, code review, evaluation, security, integration, and production troubleshooting.

- Experience with cloud platforms, enterprise integrations, DevSecOps/MLOps/LLMOps, observability, and production operations.

- Direct experience with Human Capital Management, HR technology, workforce services, talent and career platforms, People/HR shared services, or workforce analytics.

- Ability to communicate effectively with senior business, technology, risk, and governance stakeholders.

Technical Leadership:

- Lead the end-to-end technical delivery of enterprise AI capabilities from discovery and solution design through engineering, testing, deployment, and production stabilization.

- Translate ambiguous business opportunities into clear technical scope, architecture, delivery milestones, measurable outcomes, and an executable engineering backlog.

- Design Agentic AI architectures covering orchestration, routing, planning, context management, memory, knowledge retrieval, tool use, structured outputs, human oversight, and failure recovery.

- Make pragmatic trade-offs across model quality, latency, cost, privacy, security, reliability, maintainability, and time to value.

- Create or review architecture specifications, prototypes, reference implementations, code, prompts, evaluation suites, integration contracts, and operational designs.

- Embed Responsible AI, privacy, security, compliance, traceability, and auditability throughout the solution lifecycle.

Engineering Delivery and Operations:

- Lead a cross-functional team of AI/ML engineers, software engineers, data scientists, quality engineers, platform engineers, and other technical contributors.

- Own delivery planning, capacity, dependencies, risks, technical quality, release readiness, and stakeholder commitments.

- Establish engineering standards for code quality, testing, CI/CD, DevSecOps, MLOps/LLMOps, infrastructure automation, observability, and production support.

- Ensure solutions are scalable, supportable, cost-aware, and instrumented for performance, quality, safety, and business outcomes.

- Drive reuse through common patterns, shared services, accelerators, engineering playbooks, and platform capabilities.

Business, Domain, and Transformation Leadership:

- Apply strong understanding of Human Capital, HR services, employee and manager journeys, workforce policies, talent processes, and sensitive employment data where relevant.

- Lead large-scale transformation by connecting business strategy, operating processes, data, technology, governance, adoption, and measurable value.

- Communicate architecture, progress, risks, options, and decisions clearly to engineering teams and senior executives.

People and Capability Leadership:

- Build a high-accountability engineering culture with clear ownership, constructive challenge, rapid learning, and reliable execution.

- Mentor senior engineers and emerging technical leaders through architecture reviews, code reviews, pairing, delegation, and structured feedback.

- Stay current with rapidly evolving models, frameworks, evaluation methods, security risks, regulations, and market practices.

Required Technical Expertise:

Agentic AI and Generative AI:

- Deep expertise in LLM-based applications, Agentic AI systems, copilots, retrieval-augmented generation, prompt and context engineering, structured generation, and tool/function calling.

- Experience with single-agent and multi-agent orchestration, planning, routing, delegation, state, memory, recovery, and bounded autonomy.

- Hands-on experience with chunking, indexing, metadata, hybrid retrieval, reranking, vector databases, knowledge graphs, citation grounding, and authorization-aware retrieval.

- Strong understanding of transformer models, embeddings, tokenization, model selection, fine-tuning trade-offs, inference behavior, and performance optimization.

- Experience with managed and open-source models, enterprise model gateways, prompt lifecycle management, and cost and latency optimization.

- Experience with automated and human evaluation, calibrated LLM-as-judge methods, regression testing, online monitoring, and feedback loops.

Software, Cloud, Data, and Integration:

- Strong hands-on proficiency in Python and working depth in APIs, SQL, and modern AI/ML frameworks such as PyTorch, TensorFlow, LangChain, LangGraph, and Semantic Kernel.

- Experience with microservices, REST and event-driven APIs, asynchronous processing, distributed systems, caching, concurrency, resiliency, and performance engineering.

- Cloud-native architecture experience on Azure, AWS, or GCP, including containers, Kubernetes, identity and access management, secrets management, and Infrastructure as Code.

- Experience with enterprise data platforms, pipelines, lineage, data quality, schema design, real-time and batch processing, system integrations, and access controls.

- Demonstrated use of CI/CD, automated testing, DevSecOps, MLOps/LLMOps, logging, tracing, monitoring, incident response, and production reliability practices.

Preferred Qualifications:

- Advanced degree or PhD in a relevant technical discipline.

- Experience with enterprise HR platforms or service ecosystems such as Oracle HCM, Workday, ServiceNow HR Service Delivery, or comparable technologies.

- Experience with corporate governance, model risk, Responsible AI, privacy, security, compliance, internal controls, or audit readiness.

- Forward Deployed Engineering or consulting leadership experience involving rapid problem structuring, hands-on implementation, stakeholder-facing delivery, and transition from prototype to production.

- Experience building reusable AI platforms, orchestration frameworks, evaluation systems, governance tooling, or engineering standards used across multiple teams.

- Experience delivering AI transformation in healthcare, insurance, or another regulated industry.

What Success Looks Like:

- Enterprise AI capabilities are delivered with clear scope, strong engineering quality, measurable outcomes, controlled risk, and transparent operational ownership.

- AI solutions meet defined standards for groundedness, safety, security, privacy, reliability, performance, usability, and auditability.

- Reusable patterns and shared capabilities accelerate delivery while reducing duplication and maintenance burden.

- Business, technology, and governance stakeholders have confidence in the quality of technical recommendations and execution.

- Engineering talent grows in technical depth, ownership, and leadership capability.