Generative AI Tech Lead (LLMs, MLOps, AWS)

Responsibilities:

• Leadership & Team Management
-Lead, mentor, and grow a team of 5–10 ML, Data, and Software Engineers;
-Define and drive the technical roadmap for ML/AI initiatives;
-Foster a high-performance culture focused on ownership, learning, and engineering excellence;
-Work closely with Product, Data, and Platform teams to deliver end-to-end AI systems.
• Machine Learning & LLM Engineering
-Design, fine-tune, and deploy LLMs and ML models for real production use cases;
-Build systems for RAG, summarization, text generation, entity extraction, and other NLP/LLM workflows;
-Explore and implement emerging GenAI/LLM techniques and infrastructure;
-Contribute across the ML stack: NLP, deep learning, CV, RL, and classical ML.
• AWS Cloud Architecture & MLOps
-Architect and operate scalable ML/AI systems using AWS (SageMaker, Bedrock, Lambda, S3, ECS/ECR…);
-Optimize model training, inference pipelines, and data workflows for scale, cost, and latency;
-Implement MLOps/LLMOps best practices, CI/CD pipelines, monitoring, and automation;
-Ensure security, reliability, observability, and compliance across ML workloads.
• Technical Execution & Delivery Excellence
-Lead the full ML lifecycle: research - experimentation - prototyping - production - maintenance;
-Perform code reviews, lead architecture discussions, and ensure engineering best practices;
-Troubleshoot and optimize production ML systems;
-Communicate project status, risks, and decisions to stakeholders and leadership.

Qualifications:

• 5+ years of hands-on experience in Machine Learning, Deep Learning, or NLP;
• 2+ years in a technical leadership or team lead role;
• Strong expertise with LLMs (Hugging Face, OpenAI, Anthropic) and modern NLP stacks;
• Strong hands-on experience with AWS ML ecosystem (SageMaker, Bedrock, Lambda, S3, ECS/ECR);
• Excellent Python engineering skills and proficiency with PyTorch or TensorFlow;
• Experience building ML systems in production, not just research;
• Solid knowledge of MLOps/LLMOps tools, pipelines, and deployment best practices;
• Strong architectural thinking and ability to design scalable ML systems;
• Excellent communication skills and ability to lead cross-functional teams;
• Passion for mentoring engineers and raising the technical bar;
• Experience with Bedrock Agents, RAG pipelines, agentic workflows, or vector search.

What We Offer:

• 10% Annual bonus;
• Long-term B2B collaboration;
• Fully remote setup;
• Comprehensive private medical insurance or budget for your medical needs;
• Paid sick leave, vacation, and public holidays;
• Continuous learning support, including unlimited AWS certification sponsorship.

Interview stages:

• Recruitment Interview;
• Tech interview;
• HR Interview;
• HM Interview.