ML Engineer

We are seeking an experienced ML Engineer to join our team in Bangalore. This role requires 4.5 to 9 years of experience in ML engineering, MLOps, or backend engineering with a focus on production ML systems. You will work with Large Language Models (LLMs), retrieval systems, vector stores, and graph/knowledge stores. Strong software engineering fundamentals are essential, including proficiency in Python and at least one of Java, Go, or Scala, along with expertise in API design, concurrency, and testing.

You will have hands-on experience with orchestration frameworks and libraries such as LangChain, LlamaIndex, OpenAI Function Calling, and AgentKit. Familiarity with agent architectures (reactive, planning, retrieval-augmented) and safe execution patterns is required. Experience with pipelines and data technologies like Airflow/Kubeflow, Spark/Flink, Kafka/Kinesis, and robust data quality practices is crucial. We also expect experience with microservices and runtime environments, including Docker/Kubernetes, service meshes, REST/gRPC, and performance/reliability engineering.

Proficiency in model operations (Model Ops) is expected, covering experiment tracking, registries (e.g., MLflow), feature stores, A/B and shadow testing, and drift detection. Observability skills using tools like OpenTelemetry, Prometheus, and Grafana are necessary for debugging latency, tail behavior, and identifying performance bottlenecks. Cloud experience, preferably on AWS (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions/Lambda), including cost optimization, is required. Knowledge of security and compliance best practices, such as secrets management, RBAC/ABAC, PII handling, and auditability, is also a must.

We prefer candidates with a product-oriented mindset, focusing on dealer and consumer outcomes. A commitment to reliability and safety, with an understanding of guardrails, rollbacks, and Service Level Objectives (SLOs), is important. We value systems thinkers who design for multi-tenant scale, portability, and cost efficiency. Collaboration skills to bridge Applied Sciences, Product, and the Data and AI Platform, along with a willingness to document and teach, are highly valued. A pragmatic approach to automating repetitive tasks while allowing for rapid experimentation is also key.