AI/ML Manager
Remote
Duration: 6 months but can be extended.
Engineering AI/ML Manager who is a hands-on technical leader and will be responsible for delivering AI/ML solutions from design through production while managing a small team of engineers. This role balances people leadership, technical depth, and delivery accountability, and is ideal for a manager who still codes, reviews designs, and actively guides implementation. This role supports enterprise AI initiatives across machine learning, generative AI, LLM based solutions, and data driven automation, aligned with healthcare and regulated industry standards.
Required skills
Bachelor s degree in computer science, Engineering, Data Science, or related field.
7 10 years of overall engineering experience with 3+ years in AI/ML engineering.
2+ years of people or technical team leadership experience.
Strong hands-on experience with:
o Python and ML frameworks (TensorFlow, PyTorch, scikit learn)
o Cloud platforms (Azure preferred)
o ML model deployment and product ionization
Experience building and deploying LLM or GenAI solutions is strongly preferred.
Solid understanding of data pipelines, APIs, and distributed systems.
Key Responsibilities
Technical Leadership & Delivery
Lead the design, development, and deployment of AI/ML solutions in productionenvironments.
Guide architecture decisions for ML pipelines, model deployment, and inference at scale.
Review and approve technical designs, code, and model implementation approaches.
Ensure solutions meet security, reliability, scalability, and responsible AI standards.
Partner with architects and platform teams on cloud-based AI implementations (Azure preferred). People & Team Management
Manage and mentor a team of AI/ML engineers (onshore and offshore).
Support sprint planning, backlog prioritization, and delivery commitments.
Conduct performance coaching, skill development, and technical mentoring.
Foster strong collaboration across engineering, data, product, and business teams.
AI / ML Engineering Execution
Oversee development of:
o Machine learning models (supervised / unsupervised)
o Generative AI and LLM based solutions
o Retrieval Augmented Generation (RAG) pipelines
o Model deployment and monitoring workflows
Promote MLOps best practices including CI/CD, model versioning, monitoring, and retraining.
Ensure compliance with data governance, privacy, and healthcare regulations.
Stakeholder Collaboration
Translate business problems into AI/ML technical solutions.
Communicate progress, risks, and outcomes to technical and non technical stakeholders.
Partner with product, compliance, and business leaders to align AI solutions to outcomes.
Thanks & Regards,
Pankaj Singh - Team Lead
Phone #
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