Software Engineer

Sasken Technologies; Chennai; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Sasken Technologies is seeking an AI/ML Engineer to drive analytics on connected vehicle data, develop and productionize ML models, and build end-to-end ML pipelines. You will lead ML lifecycle activities, governance, and collaboration with cross-functional teams to scale AI solutions.

The role focuses on GenAI/LLM capabilities, data quality and observability, and technical leadership in AI engineering standards and partnerships with vendors.

Qualifications

• Proven experience in Python/SQL and ML frameworks (PyTorch, TensorFlow, or Scikit-learn).
• Experience with streaming data (Spark/PySpark) and Kafka/MQTT.
• Cloud ML platforms and MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI).
• Familiarity with Docker, Kubernetes, and CI/CD in production ML pipelines.
• Strong understanding of end-to-end ML lifecycle, deployment, monitoring, and governance.

Responsibilities

• Drive analytics and AI/ML capabilities on connected vehicle data from data acquisition through deployment.
• Develop and productionize ML models for predictive analytics and vehicle health.
• Build feature, training, inference, and model-serving pipelines; manage ML lifecycle.
• Establish ML lifecycle governance including model/version management and monitoring.
• Collaborate with platform, cloud, and engineering partners to scale AI solutions.

Skills

Python

SQL

PyTorch

TensorFlow

Scikit-learn

Spark/PySpark

Kafka

MQTT

AWS

Azure

GCP

MLflow

Kubeflow

SageMaker

Vertex AI

Docker

Kubernetes

CI/CD

Model deployment

Tools

PyTorch

TensorFlow

Scikit-learn

Spark

PySpark

Kafka

MQTT

AWS

Azure

GCP

MLflow

Kubeflow

SageMaker

Vertex AI

Docker

Kubernetes

CI/CD

Job description

AI/ML Engineer

Job Description:

Role Purpose

Drive the engineering of connected vehicle data, analytics, and AI/ML capabilities, taking use cases from vehicle data and experimentation through production deployment and continuous improvement.

Key Responsibilities

Connected Vehicle Data & Analytics: Build analytics capabilities using vehicle telemetry, diagnostics, usage, and operational data.

AI/ML Engineering: Develop and productionize ML models for predictive analytics, vehicle health, battery intelligence, and vehicle/customer insights. Build feature, training, inference, and model-serving pipelines.

MLOps & Production AI: Establish the ML lifecycle from data and training through validation, deployment, monitoring, and retraining. Implement model/version management, drift detection, and reusable ML services.

AI Platform & GenAI: Enable relevant GenAI/LLM, RAG, and AI-Agent use cases and establish reusable AI engineering patterns.

Data & AI Governance: Ensure data quality, lineage, security, observability, and appropriate AI governance.

Technical Leadership: Define data/AI engineering standards, evaluate technologies, work with vehicle/platform/cloud teams, and guide implementation partners.

Must-Have Skills

Strong Python and SQL; PyTorch, TensorFlow and/or Scikit-learn.

Spark/PySpark and streaming technologies such as Kafka, MQTT or equivalent.

AWS, Azure or GCP; MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI.

Docker, Kubernetes, CI/CD, large-scale data processing.

Strong understanding of ML model deployment, monitoring, and the end-to-end ML lifecycle.

Strongly Preferred

Connected Vehicle, Automotive, IoT, or Mobility experience; Vehicle Telemetry / Time-Series Data.

Predictive Maintenance, Vehicle Health, Battery Analytics, EV Data, or Digital Twin.

Real-time Streaming Analytics and Large-scale Fleet Data.

GenAI / LLM / RAG / AI Agents and experience taking AI solutions from PoC to Production.

Experience working with Engineering Partners / Vendors.