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Machine Learning Engineer
Weekday —Bengaluru, Karnataka
- ₹28,00,000 - ₹85,00,000 a year
- Full-time
- System design
- Java
- Master's degree
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Machine Learning Engineer

Weekday -
Bengaluru, Karnataka

Quick apply

Job details

Full-time
₹28,00,000 - ₹85,00,000 a year
1 day ago

Qualifications

CI/CD
Go
Law
Kubernetes
Spark
System design
Java
Master's degree
Databases
Microservices
RBAC
AWS
Docker
Machine learning
Distributed systems
Scala
Continuous integration
SDKs
REST
Natural language processing
APIs
A/B testing
Kafka
SaaS
AI
gRPC
Graph databases
Python
Software architecture

Full job description

This role is for one of our clients

Industry: Software Development

Seniority level: Mid-Senior level

Min Experience: 5+ years

Location: Bengaluru

JobType: full-time

We are looking for a Machine Learning Engineer to build and operate the production infrastructure that transforms machine learning research into scalable, reliable, and low-latency AI services. You will partner closely with Applied Science, Product, and Platform Engineering teams to operationalize machine learning models, LLM-powered applications, and agentic workflows that power real-world enterprise products.

This role focuses on building production-ready ML systems, developing MLOps infrastructure, and ensuring AI services are secure, observable, cost-efficient, and highly available. You'll play a key role in enabling both traditional machine learning models and modern generative AI applications to move seamlessly from experimentation into production.

Requirements

Key Responsibilities

Production Machine Learning Systems

Convert prototype machine learning models into production-grade, scalable services with well-defined API interfaces.
Deploy and optimize models across various domains including predictive analytics, recommendation systems, forecasting, NLP, and generative AI.
Refactor, containerize, version, deploy, and continuously monitor machine learning models for production readiness.
Collaborate with Applied Science teams to improve model performance, scalability, and operational efficiency.

LLM & Agentic AI Infrastructure

Integrate AI applications with enterprise LLM gateways, model routing systems, and prompt management frameworks.
Support retrieval-augmented generation (RAG), vector search, and knowledge retrieval architectures.
Build and maintain agentic AI workflows, orchestration frameworks, and safe execution patterns.
Implement prompt versioning, experimentation, A/B testing, dynamic orchestration, and AI safety guardrails.

MLOps & Platform Engineering

Design and maintain CI/CD pipelines for machine learning models and AI services.
Build batch and streaming data pipelines using modern orchestration and distributed processing frameworks.
Develop online feature pipelines, feature stores, model registries, and experiment tracking infrastructure.
Automate model lifecycle management, deployment workflows, rollback strategies, and continuous delivery.

Microservices & Distributed Systems

Develop high-performance inference services using REST and gRPC APIs.
Build scalable microservices supporting low-latency online predictions.
Implement schema versioning, structured outputs, and API reliability standards.
Optimize service performance to consistently meet stringent latency and availability targets.

Monitoring, Reliability & Observability

Implement comprehensive monitoring across AI systems, including traces, logs, metrics, model performance, and infrastructure health.
Detect model drift, data quality issues, feature degradation, and operational anomalies.
Design resilient systems with autoscaling, caching, retries, circuit breakers, fallback mechanisms, and graceful degradation.
Track infrastructure utilization, latency, cost, and AI service quality through production dashboards.

Developer Experience & Enablement

Create reusable SDKs, templates, command-line tools, and deployment frameworks.
Build sandbox environments and documentation that simplify AI application development.
Collaborate with engineering teams to establish best practices for production ML, MLOps, and AI engineering.
Mentor engineers and contribute to improving platform standards and development workflows.

Required Qualifications

5–11+ years of experience in Machine Learning Engineering, MLOps, Platform Engineering, or Backend Engineering supporting production ML systems.
Strong software engineering skills with expertise in Python and at least one of Java, Go, or Scala.
Solid understanding of distributed systems, concurrency, API design, testing, and scalable software architecture.
Experience deploying and operating production machine learning services.
Hands-on experience with orchestration frameworks and LLM tooling such as LangChain, LlamaIndex, OpenAI Function Calling, Agent frameworks, or similar technologies.
Knowledge of retrieval-augmented generation (RAG), vector databases, knowledge graphs, and AI agent architectures.
Experience building data pipelines using Airflow, Kubeflow, Spark, Flink, Kafka, or similar technologies.
Strong experience with Docker, Kubernetes, microservices, REST APIs, and gRPC services.
Familiarity with experiment tracking, model registries, feature stores, drift detection, A/B testing, and shadow deployments.
Experience implementing observability using tools such as OpenTelemetry, Prometheus, Grafana, or similar monitoring platforms.
Experience deploying cloud-native applications on AWS or comparable cloud environments.
Understanding of security best practices including RBAC, secrets management, audit logging, and PII protection.

Preferred Qualifications

Experience building enterprise AI platforms or large-scale MLOps infrastructure.
Knowledge of vector databases, retrieval systems, and knowledge graph technologies.
Experience supporting LLM-powered applications, AI agents, and autonomous workflows.
Familiarity with cloud cost optimization and multi-tenant SaaS architectures.
Strong understanding of production reliability engineering and distributed system design.

Ideal Candidate Profile

The ideal candidate:

Thinks beyond models and focuses on delivering measurable business outcomes.
Prioritizes reliability, scalability, security, and operational excellence.
Enjoys designing production systems that balance performance, cost, and maintainability.
Works effectively across Applied Science, Product, and Engineering teams.
Believes in automation, developer productivity, and platform engineering best practices.
Documents processes clearly and enjoys mentoring other engineers.

Why Join Us?

Join a team building next-generation AI infrastructure that enables enterprise-scale machine learning, LLM-powered applications, and intelligent automation. You'll help shape production AI platforms that power real-world products while working with modern MLOps technologies, distributed systems, and cutting-edge generative AI.

Must-Have Skills

Machine Learning Engineering
Python
MLOps
Kubernetes
Docker
REST APIs
Distributed Systems
CI/CD
LLM Applications

Good-to-Have Skills

Machine Learning
Python
LangChain
LlamaIndex
Kafka
Spark
Airflow
Kubeflow
MLflow
Vector Databases
Retrieval-Augmented Generation (RAG)
AWS

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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