Applied AI ML Lead - AI Agents & Agentic Systems
Applied AI ML Lead - AI Agents & Agentic Systems
Posted 8 days ago
SENIORITY
Lead
About the role
- Serve as a subject matter expert on a wide range of ML techniques and optimizations.
- Provide in-depth knowledge of ML algorithms, frameworks, and techniques.
- Enhance ML workflows through advanced proficiency in large language models (LLMs) and related techniques.
- Conducting experiments using latest ML technologies, analyzing results, tuning models
- Hands on coding to bring the experimental results into production solutions by collaborating with engineering team. Owning end to end code development in python for both proof of concept/experimentation and production-ready solutions.
- Optimizing system accuracy and performance by identifying and resolving inefficiencies and bottlenecks. Collaborates with product and engineering teams to deliver tailored, science and technology-driven solutions.
- Integrate Generative AI within the ML Platform using state-of-the-art techniques.
- Drives decisions that influence the product design, application functionality, and technical operations and processes.
- Required qualifications, capabilities, and skills:
- MS and/or PhD in Computer Science, Machine Learning, or a related field, with at least 5 years of applied machine learning experience.
- At least 5 year's experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.
- At least 5 years' experience in applying data science, ML techniques to solve business problems.
- Solid background in Natural Language Processing (NLP) and Large Language Models (LLMs)
- Experience with machine learning and deep learning methods.
- Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow.
- Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search).Ability to work on tasks and projects through to completion with limited supervision.
- Passion for detail and follow through. Excellent communication skills and team player
- Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.
- Preferred qualifications, capabilities, and skills:
- Experience with Ray, MLFlow, and/or other distributed training frameworks.
- In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
- Advanced knowledge in Reinforcement Learning or Meta Learning.
- Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.
- Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
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Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
