Ai Ml Engineer

Primary Responsibilities:

• Design, build, and deploy scalable AI/ML solutions
• Stay updated with the latest advancements in AI/ML techniques and tools, and identify opportunities to apply them to enhance existing solutions
• Collaborate with product, engineering, and business teams
• Contribute to MLOps practices and automation
• Review and guide junior engineers' work
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
  
  Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities. Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle.

Qualifications -

Required Qualifications:

• Graduate degree or equivalent experience
• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related field - or equivalent practical experience
• 5+ years of professional software engineering experience, with hands-on AI/ML development in production environments
• 5+ years of hands-on experience building AI/ML solutions, including LLMs, Generative AI, and traditional ML models, Design, build, and deploy LLM powered solutions, including RAG pipelines, agentic workflows, decisioning agents, and orchestration layers
• Experience using Git, pull requests, and collaborative development workflows
• Experience on agentic AI frameworks like langraph, deep evals
• Experience deploying and operating LLM-powered services at scale 
• Practical experience designing and implementing RAG architectures and working with LLMs / GenAI frameworks
• Knowledge of Cloud platforms (Azure/AWS/GCP - preferred)
• Solid understanding of machine learning fundamentals, including training/evaluation workflows, metrics, and common algorithms
• Familiarity with AI-assisted development tools (e.g., GitHub Copilot) and how to use them effectively in day-to-day engineering work 
• Solid proficiency in Python, with experience building maintainable, testable, and scalable systems
• Advanced Python and ML frameworks (PyTorch, TensorFlow)
• Solid grasp of ML algorithms, evaluation, and optimization