AI/ML (Java, APIs, Microservices)

About the Role:

We are seeking a Senior ML Platform Engineer to architect and build real-time AI systems that process streaming financial data. You'll be responsible for developing scalable machine learning infrastructure that powers intelligent financial recommendations, personalized user experiences, and real-time analytics.

Location: Navi Mumbai

Experience: 3-8 years

Key Responsibilities

Real-Time Data Processing

• Design and implement

  Apache Kafka-based streaming pipelines

  for high-volume financial data
• Build robust

  event-driven architectures

  to process real-time market data and user interactions
• Integrate with

  microservices

  and external financial APIs for seamless data flow
• Implement

  stream processing logic

  using Kafka Streams or Apache Flink

AI/ML Infrastructure

• Deploy and maintain

  machine learning models

  in production environments
• Build

  real-time inference pipelines

  for AI-powered financial recommendations
• Implement

  MLOps practices

  including model versioning, monitoring, and automated retraining
• Optimize

  model serving infrastructure

  for low-latency predictions

Platform Development

• Architect

  scalable data platforms

  on cloud infrastructure (Azure/AWS)
• Develop

  APIs and microservices

  for ML model integration
• Implement

  monitoring and alerting systems

  for data quality and model performance
• Ensure

  security and compliance

  with financial industry standards

Cross-functional Collaboration

• Work closely with

  data science teams

  to productionize ML models
• Collaborate with

  backend engineers

  on system integration
• Partner with

  product teams

  to understand business requirements
• Contribute to technical decision-making

Technical Expertise

• 3+ years

  of experience in building production ML systems

• Strong expertise in Apache Kafka

  and event streaming architectures

• Proficiency in Python and Java

  for ML and streaming applications

• Experience with ML frameworks

  (TensorFlow, PyTorch, Scikit-learn)

• Cloud platforms knowledge

  (Azure, AWS, or GCP)

• Database technologies

  (PostgreSQL, MongoDB, Redis)

• Containerization and orchestration

  (Docker, Kubernetes)

Engineering Practices

• Strong software engineering principles

  and clean coding practices

• Experience with CI/CD pipelines

  and DevOps practices

• API design and microservices architecture

• Performance optimization

  for high-throughput systems

• Version control

  (Git) and collaborative development

Preferred Qualifications

Advanced AI/ML Skills

• Experience with

  Large Language Models (LLMs)

  and modern AI architectures
• Knowledge of

  recommendation systems

  and personalization engines

• Vector databases

  and similarity search systems (Pinecone, Weaviate)

• Real-time feature engineering

  and online learning systems

Streaming & Infrastructure

• Apache Flink

  or advanced Kafka ecosystem tools (Connect, Schema Registry)

• Time-series databases

  (InfluxDB, TimescaleDB) for financial data

• Message queue systems

  beyond Kafka (RabbitMQ, Apache Pulsar)

• Model optimization

  techniques for production deployment