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Machine Learning Engineer II

Okda Solutions
5 - 10 Years
Remote

Posted on: 27/04/2026

Job Description

Description : Immediate Joiners or less than 30 days notice period

Roles and Responsibilities :


We are looking for a skilled and motivated Machine Learning Engineer to join our engineering team. This is a mid-level role focused on building and maintaining a multi-agent system powering an ML-driven assistant. Ideal candidates are passionate about solving complex ML problems and building scalable, reliable, and safe systems in dynamic environments.

Key Responsibilities :

- Design, develop, and maintain a multi-agent system supporting ML-driven workflows.

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines for low latency, high accuracy, and context-aware outputs.

- Develop scalable, safe, and predictable ML solutions aligned with goal-oriented systems.

- Collaborate with data scientists, engineers, and product teams to integrate ML algorithms and LLMs into production systems.

- Solve challenges related to real-time ML applications.

- Monitor, evaluate, and improve ML model performance in production environments.

- Research and evaluate new tools, frameworks, and ecosystems to enhance ML capabilities.

- Explore cloud ecosystem expansion beyond Azure where relevant.

- Implement best practices for ML lifecycle management including versioning, retraining, and deployment.

- Ensure compliance with data privacy and security standards in ML workflows.

Required Qualifications :

- 5+ years of experience in Machine Learning Engineering or related field.

- Strong experience building production-grade ML systems.

- Solid understanding of multi-agent systems and distributed architectures.

- Hands-on experience integrating LLMs into production workflows.

- Proficiency in Python and frameworks such as LangGraph and Google ADK.

- Experience with cloud platforms, preferably Azure.

- Proven ability to solve challenges related to latency, accuracy, and safety.

- Strong foundation in data structures, algorithms, and software engineering.

- Familiarity with MLOps practices including model monitoring, versioning, and retraining.

- Strong communication and collaboration skills.

- Interest in building impactful technology solutions.

- Ability to work in ambiguous and evolving problem spaces.


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