Senior AI/ML Engineer Experience : 9 -15 years

Location : Bangalore, Kochi, Trivandrum We're looking for a hands-on Senior AI/ML Engineer with strong technical expertise. What we're looking for:

Individual Contributors (ICs) with hands-on AI/ML experience.

Leads/Managers are welcome if they are actively involved in architecture, development, and solution delivery.

Not looking for candidates focused primarily on people management or task allocation. Preferred domain experience:

Travel

Hospitality

Cruise

Logistics Also considered

Retail

FinTech Note: Candidates with primarily Automotive domain experience may not be the best fit due to the different nature of AI use cases. Criteria & Required Signal Production ML experience - Has deployed AI/ML models beyond notebooks/PoCs

Strong Python + SQL - Can write production-grade Python and complex SQL

MLOps exposure - Docker, APIs, CI/CD, monitoring, model deployment, retraining, drift monitoring

Cloud experience - AWS/Azure preferred; GCP acceptable

GenAI / RAG implementation - Built RAG pipelines, not just prompt engineering

Agentic AI / tool use - Experience with LangChain, LangGraph, MCP/tools, function calling, or NL-to-SQL systems Role Summary The Senior Machine Learning Engineer will be responsible for building, deploying, and scaling machine learning solutions in production environments. This role focuses on bridging the gap between data science and engineering by operationalizing models, building robust data and ML pipelines, and ensuring reliability, scalability, and performance of AI/ML systems. The role will also incorporate emerging approaches such as Generative AI where relevant to enhance solution capabilities.

Responsibilities

ML Engineering & Productionization

Model Deployment: Deploy machine learning models into production using scalable and reliable architectures.

Pipeline Development:



Build and maintain end-to-end data and ML pipelines for training, validation, and inference.

System Integration: Integrate ML models with enterprise applications, APIs, and downstream systems.

Performance Optimization: Ensure models and pipelines are optimized for latency, throughput, and cost. MLOps & Lifecycle Management

MLOps Implementation: Establish and follow best practices for CI/CD, model versioning, monitoring, and retraining.

Model Monitoring: Implement monitoring for model performance, data drift, and system health.

Automation: Automate workflows for model training, testing, and deployment.

LLMOps Awareness: Apply emerging practices for managing and deploying LLM-based applications where relevant. Platform & Data Engineering Collaboration

Data Integration: Work closely with data engineering teams to ensure availability and quality of data for ML use cases.

Scalable Infrastructure: Leverage cloud platforms and distributed systems (e.g., Spark) to build scalable solutions.

Feature Engineering Pipelines: Build reusable and production-grade feature pipelines. Generative AI Enablement (Where Relevant)

LLM Integration: Develop and integrate LLM-based solutions (e.g., APIs, embeddings, RAG pipelines) into applications.

Application Development: Support use cases such as conversational interfaces, copilots, and knowledge assistants.

Evaluation & Guardrails: Implement evaluation, monitoring, and guardrails for GenAI applications.

Technical

Leadership & Best Practices

Code Quality: Ensure high-quality, maintainable,



and well-documented code.

Design Standards: Contribute to defining architecture patterns and engineering standards for ML systems.

Collaboration: Work closely with data scientists to productionize models and improve deployability.

Mentorship: Guide junior engineers on ML engineering and MLOps best practices. Requirements

Education and Experience

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

8–12 years of experience in software engineering, data engineering, or ML engineering roles.

Technical

Skills

Robust programming skills in Python (mandatory) and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

Experience with model deployment frameworks (e.g., Flask/FastAPI, Docker, Kubernetes).

Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).

Experience with data engineering tools and distributed processing (e.g., Spark, Kafka).

Familiarity with cloud platforms (AWS, GCP, or Azure).

Working knowledge of Generative AI concepts (LLMs, embeddings, vector databases, RAG). Core Competencies

Engineering Mindset: Strong focus on scalability, reliability, and maintainability.

Problem Solving: Ability to troubleshoot complex system and performance issues.

Collaboration: Ability to work across data science, engineering, and product teams.

Communication: Ability to explain technical implementations to diverse stakeholders.

Preferred

Qualifications

Experience building end-to-end ML platforms or frameworks.

Exposure to real-time/streaming ML systems.

Experience with LLM-based application development frameworks (e.g., LangChain) and vector databases.

Understanding of security, governance, and responsible AI practices.

Experience in travel, transportation, or logistics domains.

📌 AI/ML (Bengaluru)
🏢 IBS Software
📍 Bengaluru