ML Engineer

Tekion; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Tekion is seeking an experienced ML engineering/MLOps professional in Bengaluru to build production ML systems with LLMs, retrieval capabilities, and vector/graph stores. Strong software fundamentals in Python and at least one of Java/Go/Scala are required, along with API design and testing.

You will work with orchestration tools like LangChain, LlamaIndex, and OpenAI Function Calling, and collaborate across multi-tenant data platforms.

Qualifications

• 6 - 9 years in ML engineering/MLOps or backend engineering with production ML.
• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores.
• Strong software engineering fundamentals: Python plus one of Java/Go/Scala, API design, concurrency, and testing.
• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc.).
• Knowledge of agent architectures (reactive, planning, and retrieval-augmented agents) and safe execution patterns.
• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices.
• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC, performance, and reliability engineering.
• Model ops: experiment tracking, registries (e.g. MLflow), feature stores, A/B and shadow testing, and drift detection.
• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots.
• Cloud: AWS preferred (IAM, ECS/EKS, S3 RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience.
• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability.

Skills

ML engineering/MLOps

LLMs

Retrieval systems

Vector stores

Graph/knowledge stores

Python

Java/Go/Scala

API design

Concurrency

Testing

Orchestration frameworks

Agent architectures

Pipelines

Data quality

Microservices

REST/gRPC

Observability

Cloud

Security/compliance

Tools

LangChain

LlamaIndex

OpenAI Function Calling

AgentKit

Airflow

Kubeflow

Spark

Flink

Kafka

Kinesis

Docker

Kubernetes

Service meshes

REST

gRPC

OpenTelemetry

Prometheus

Grafana

AWS

Job description

Requirements:

• 6 - 9 years in ML engineering/MLOps or backend engineering with production ML.
• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores.
• Strong software engineering fundamentals: Python plus one of Java/Go/Scala, API design, concurrency, and testing.
• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc. ).
• Knowledge of agent architectures (reactive, planning, and retrieval-augmented agents) and safe execution patterns.
• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices.
• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC, performance, and reliability engineering.
• Model ops: experiment tracking, registries (e. g., MLflow), feature stores, A/B and shadow testing, and drift detection.
• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots.
• Cloud: AWS preferred (IAM, ECS/EKS, S3 RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience.
• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability.

Preferred Mindset:

• Product-oriented: You measure success by dealer and consumer outcomes, not just technical metrics.
• Reliability and safety first: You move fast with guardrails, rollbacks, and clear SLOs.
• Systems thinker: You design for multi-tenant scale, portability, and cost efficiency.
• Collaborative: You translate between applied sciences, product, and the data and AI platform; you document and teach.
• Pragmatic: You automate the 80% and leave room for rapid experimentation.