Senior AI/ Machine Learning Engineer

About the role

This position involves shaping the future of agentic AI within our decision intelligence platform. You will be responsible for architectural decisions that guide the development of autonomous agents, ensuring their reliability and effectiveness in real-world scenarios.

Key facts

What you'll do

• Define the technical strategy for agentic systems, including architecture selection, design documentation, and decisions on build versus buy and framework adoption.
• Take ownership of the agentic platform's end-to-end functionality, covering multi-step reasoning, tool integration, sub-agent coordination, memory management, and long-term autonomous operation.
• Establish and maintain rigorous evaluation standards for agentic systems, encompassing offline and online testing, golden datasets, LLM-as-judge pipelines, and regression testing tied to model and prompt versions.
• Develop a shared foundation of tools, skills, and infrastructure to enable efficient problem-solving across the platform.
• Optimize agent performance, cost, and reliability through careful management of context, parallel processing, model selection, and inference techniques.
• Mentor fellow engineers, conduct thorough design and code reviews, and elevate the standard for production-ready non-deterministic systems.
• Integrate agent security as a core design principle, addressing tool permissions, sandboxing, prompt injection, and potential impact of agent misbehavior.
• Collaborate with Data Science, Engineering, DevOps, and Product teams to translate business needs into functional systems and provide critical feedback on problem framing.
• Assess emerging AI technologies and provide informed recommendations for adoption.

Requirements

• Bachelor of Engineering or Bachelor of Technology in Computer Science, Computer Engineering, or a related discipline.
• 5 to 8 years of experience in software engineering and architecture, with a minimum of 3 years focused on designing and deploying ML or LLM-based systems.
• At least 12 months of experience building agentic or LLM-powered systems that have been deployed to production.
• Proven ability to set technical direction, evidenced by design documents, architectural ownership, and experience managing systems from architecture through production reliability.
• Strong systems thinking capabilities, with the ability to abstract and generalize problems across business domains.
• Proficiency with agentic coding tools such as Claude Code or similar, with a deep understanding of model strengths and weaknesses.
• Expertise in current agentic engineering practices, including context engineering, tool and skill design, sub-agent patterns, agent memory, evaluation methodologies, and retrieval-augmented generation.
• Well-defined and defensible opinions on system evaluation, understanding the limitations of benchmarks and the importance of practical deployment testing.
• Fluency in Python and experience with production service frameworks like FastAPI.
• Solid understanding of distributed systems, experience with large datasets and ML pipelines (e.g., Ray, Spark).
• Hands-on experience with PyTorch, Hugging Face, scikit-learn, and pandas.
• Familiarity with containerized microservices (Docker, Kubernetes) and CI/CD practices (Git, Jenkins, Jira).
• Adaptability and humility regarding code and frameworks; experience with orchestration frameworks like LangGraph is beneficial but not the primary skill.
• Excellent written and verbal communication skills, with the ability to articulate technical concepts and influence decision-making.

Nice to have

• Experience leading technical initiatives or mentoring engineers.
• GoLang for high-performance components.
• Experience with vector databases such as Pinecone, Weaviate, FAISS, or pgvector.
• Familiarity with durable execution platforms like Temporal.
• Experience with event streaming and caching technologies (Kafka, Pulsar, Redis).
• Knowledge of agent observability and experiment tracking tools (Langfuse, LangSmith, OpenTelemetry, MLflow, W&B, DVC).
• Experience with fine-tuning models and the judgment to apply it appropriately.
• Understanding of multi-modal AI, integrating text, image, and structured data.
• Experience with serverless AI infrastructure on AWS, GCP, or Azure.
• Familiarity with enterprise-grade constraints like multi-tenancy, data isolation, compliance, and regulated deployments.

Skills & tools

• Python
• FastAPI
• PyTorch
• Hugging Face
• scikit-learn
• pandas
• Ray
• Spark
• Docker
• Kubernetes
• Git
• Jenkins
• Jira
• Claude Code (or equivalent)
• LangGraph (or comparable)

Practical notes

• Competitive salary and company stock options.
• Comprehensive medical, Group Medical Insurance, Term Insurance, and Accidental Insurance.
• Paid time off and maternity leave.
• Unlimited access to online professional development courses.
• Flexible working environment.
• Fully-stocked kitchen with snacks and beverages when working from the office.