AI Engineering Manager_Machine Learning

Role: Sr AI Engineering Manager_Machine Learning

Experience: - Minimum 10+ Years

Location: - USA Remote

Hiring Type: - C2C Visa Independent

We are looking for a AI Engineer to design, build, and deploy high-quality AI-powered features with end-to-end ownership from prototyping to production, ensuring reliable, scalable, and impactful AI solutions.

Responsibilities: -

End-to-End AI Feature Ownership

Design and implement AI-powered features (LLM workflows, copilots, and agent-based systems with tool use and multi-step reasoning)

Own the Full Lifecycle: prototyping evaluation production deployment iteration

Ensure solutions are reliable, performant, and aligned with product needs

AI System Implementation

Build and optimize prompt pipelines for specific use cases

Build retrieval systems (embeddings, chunking, ranking)

Implement RAG-based workflows where needed

Iterate on outputs to improve quality, accuracy, and consistency

Design scalable and cost-efficient AI architectures for production workloads

Select and evaluate models (hosted vs open-source) based on use case constraints

Agent-Based Systems (AgentCore)

Design and build agentic workflows capable of multi-step reasoning and decision-making

Integrate agents with tools, APIs, and internal systems to perform real-world actions

Implement planning, execution, and reflection loops for complex tasks

Manage context, memory, and state across multi-step interactions

Balance deterministic workflows vs. agent autonomy for reliability and control

Experimentation & Evaluation

Run structured experiments to compare approaches (prompting, retrieval, models)

Define and track key metrics for AI performance (quality, latency, cost)

Debug and improve non-deterministic system behavior

Build and maintain evaluation datasets and benchmarks

Implement automated evaluation pipelines for continuous improvement

Collaboration & Contribution

Drive technical direction and influence AI adoption across teams

Partner with product managers and designers to scope AI features

Contribute to shared patterns and reusable components

Participate in code reviews and design discussions

Support and mentor mid-level engineers where needed

AI Reliability, Safety & Governance

Design guardrails to ensure safe and reliable AI behavior

Mitigate hallucinations, prompt injection, and model misuse

Ensure compliance with data privacy and enterprise requirements

Implement monitoring and observability for AI systems in production

Implement guardrails for agent actions (tool access control, execution boundaries)

Prevent failure cascades in multi-step agent

Educational Qualifications: -

Engineering Degree BE/ME/BTech/MTech/BSc/MSc.

Technical certification in multiple technologies is desirable.

Skills: -

Mandatory skills

Core AI Skills

Strong understanding of LLM capabilities and limitations

Experience with prompt engineering and structured output design

Hands-on experience with embeddings and vector search

Familiarity with RAG architectures and when to apply them

Experience designing agent-based architectures (AgentCore concepts)

Understanding of tool use, planning strategies, and memory mechanisms in LLM systems

Engineering Skills

5+ years of related work experience

Solid backend/system design fundamentals

Experience building and deploying production-grade systems

Ability to debug complex issues, including probabilistic outputs

Comfort working with APIs, pipelines, and data flows

Product Thinking

Ability to translate user needs into effective AI solutions

Strong intuition for balancing quality, latency, and cost

Focus on delivering measurable product impact

Collaboration

Communicates clearly across engineering and product teams

Contributes to team knowledge and shared practices.

Good to have skills

Evaluate agent performance across multi-step tasks (task success rate, error propagation)

Debug and optimize agent decision-making and tool selection behavior