Accolite

Lead ML Integration Engineer

Posted On

July 21, 2025

Location

Bangalore, Karnataka

Experience Level

6 - 9 Years Exp.

Job Type

Full Time

Apply By

Nov. 8, 2025

Job Description

We seek individuals with highly developed conceptual, strategic, and analytical skills, capable of striking a balance between visionary thinking and practical solutions. The ability to comprehend, inspire, and mobilize others is crucial. A business-oriented mindset coupled with effective storytelling will drive your success. We are looking for self-starters ready to take on responsibilities with enthusiasm.

Your Role

The Lead ML / Integration Engineer role focuses on building and integrating Generative AI and Agentic AI solutions into enterprise environments. You will work closely with data scientists, architects, and DevOps teams to design, implement, and optimize AI pipelines and infrastructure.

Required Qualifications

Bachelor’s degree in Computer Science, Engineering, or a related field.
7+ years of experience in Machine Learning engineering or AI system integration.
Hands-on experience with OpenShift, Docker, Kubernetes.
Knowledge of cloud platforms (e.g. AWS, GCP) is a must-have.
Exposure to data and network security and compliance in AI systems.
Understanding of Generative AI and Agentic AI concepts.
Experience with LLM prompt engineering, or RAG pipelines.
Knowledge of API integration and microservices architecture.
Proficiency in Python used both for ML and automation tasks
Good knowledge of Bash and Unix/Linux command-line toolkit is a must-have.
Knowledge of Workflow Orchestrator, such as Ctrl-M
Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
Experience with Observability framework, such as Langfuse, Elastic Stack, Grafana, OpenTelemetry.

Key Responsibilities

Design and implement scalable ML pipelines for Generative and Agentic AI applications.
Integrate AI models into production environments using containerized platforms such as OpenShift and Kubernetes.
Collaborate with cross-functional teams to understand AI workflows and translate them into robust engineering solutions.
Develop and maintain automation scripts using Linux shell scripting, Python, or other relevant tools.
Ensure seamless deployment and integration of AI services in cloud environments (e.g., AWS, Azure, GCP).
Implement and maintain network security protocols to safeguard AI systems and data pipelines.
Monitor and optimize system performance, reliability, and scalability.
Support CI/CD processes and infrastructure for AI model deployment and updates.

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