Senior AI Engineer - ML & Generative AI

You will work in small, high-impact delivery teams (2-3 engineers per initiative) and spend the majority of your time (~70-75%) building systems end to end, while also contributing to solution design, technical decision-making, and cross-functional collaboration., * Partner with business and product stakeholders to translate real-world problems into practical AI solutions.

• Determine when to apply:
• Traditional ML approaches (classification, regression, clustering, recommendation systems)
• LLM / GenAI approaches, including agentic workflows
• Evaluate and communicate trade-offs across accuracy, cost, latency, scalability, and operational complexity.
• Design iterative AI workflows and propose alternative solution approaches where applicable.

Hands-on Engineering & Delivery (70-75%)

• Build and own end-to-end AI systems, including:
• Data ingestion and processing pipelines
• Feature engineering and prompt construction
• ML and LLM integration and orchestration
• API-based AI services for downstream consumption
• Deploy and harden production AI systems with:
• Error handling and fallback mechanisms
• Guardrails, safety controls, and exception handling
• Observability (logging, metrics, tracing, dashboards)
• Ensure production readiness through:
• Performance tuning and latency optimization
• Cost management and optimization strategies
• Scalability and reliability planning
• Implement AI system controls such as:
• Input validation and prompt injection mitigation
• Configurable policies and kill switches
• Transition PoCs into production-grade systems through refactoring, testing, and system hardening.

ML & Generative AI Expertise

• Apply strong fundamentals in traditional ML, including supervised and unsupervised learning techniques.
• Build and deploy GenAI solutions, with experience across at least one or two real-world LLM implementations.
• Work with modern LLMs (e.g., OpenAI, Claude, Gemini, Llama or equivalent models).
• Design and implement RAG (Retrieval-Augmented Generation) architectures.
• Apply prompt engineering, evaluation techniques, and iterative optimization.
• Build and evolve tool-based and agentic workflows, including multi-agent systems.
• Use agent orchestration frameworks (e.g., LangChain, LangGraph, or equivalent custom systems).

Collaboration & Technical Leadership (25-30%)

• Act as a senior technical contributor within small delivery teams.
• Debug complex AI system behavior and production issues beyond prompt-level tuning.
• Contribute to architectural and design decisions alongside architects and platform teams.
• Collaborate closely with:
• Product managers and business stakeholders
• Platform, cloud, and infrastructure teams
• Uphold strong software engineering practices and delivery discipline.

We are seeking a hands-on Senior AI Engineer with a strong foundation in traditional Machine Learning and practical, real-world experience building and deploying LLM- and GenAI-driven systems . This role focuses on designing, engineering, and hardening production-grade AI solutions that are embedded into business workflows-not research prototypes., Qualifications: Software & Systems Engineering

• 10-12 years of overall software engineering experience, including prior work as an ML Engineer or equivalent.
• Strong backend development skills (Python, Java, Node.js, or similar languages).
• Experience designing and building REST or gRPC-based services.
• Solid understanding of distributed system design.
• Containerization and orchestration experience (Docker, Kubernetes).

AI / ML

• Hands-on experience across traditional ML and modern GenAI systems.
• Proficiency with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalents.
• Experience building or deploying:
• ML-driven production systems
• LLM-based applications
• Ability to select ML vs. LLM-driven approaches based on business and operational constraints.

Cloud & DevOps

• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
• Experience with CI/CD pipelines and deployment automation.
• Understanding of model, code, and configuration versioning best practices.

Observability & Production Readiness

• Experience implementing logging, monitoring, and tracing for production systems.
• Familiarity with system resilience patterns such as:
• Rate limiting
• Failover strategies
• Kill-switch mechanisms

Problem Solving & Mindset

• Strong ability to solve ambiguous, real-world engineering problems.
• Comfortable working in fast-moving, iterative environments.
• Ownership mindset with a bias toward practical, scalable solutions.

Communication & Collaboration

• Experience working in cross-functional teams.
• Ability to clearly articulate technical and business trade-offs, including:
• LLM vs traditional ML
• Build vs buy decisions
• Speed vs robustness

3.73.7 out of 5 stars United States Hybrid work $100,000 - $120,000 a year - Full-time