Senior AI Engineer

Key Responsibilities

• 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.
• 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.

Required Skills & Experience

• Software & Systems Engineering: 8-12 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.

Good to Have

• Experience with enterprise AI platforms or internal AI frameworks.
• Prior production experience with: Agentic architectures, Multi-agent systems, RAG-based systems at scale.
• Exposure to AI governance, safety, and compliance considerations.
• Experience mentoring junior engineers or owning technical modules.
• Hands-on experience optimizing performance and cost for AI workloads.