Role: Lead ML Engineer
Location: Atlanta, GA, USA
Job Description
We are seeking a highly skilled and communicative Machine Learning Engineer (8+ years of experience) to join our AI team.
In this role, you will bridge the gap between cutting-edge AI research and practical, user-facing applications.
You will be responsible for designing, building, and deploying Generative AI models (LLMs, Diffusion Models) to solve complex business problems, while effectively communicating technical advancements to non-technical stakeholders.
The ideal candidate is both a hands-on coder passionate about ML, Gen AI and a proactive collaborator who excels at explaining complex AI concepts to diverse teams.
Roles & Responsibilities
Key Responsibilities
GenAI Development: Architect, fine-tune, and deploy Large Language Models (LLMs) and Generative AI techniques (e.g., RAG, PEFT/SFT) to improve business applications.
Production Deployment: Build and maintain high-performance, scalable ML pipelines and GPU-based inference systems in cloud environments (AWS/Google Cloud Platform/Azure).
Collaboration & Communication: Work closely with product managers, data scientists, and engineers to translate business requirements into technical specifications.
Stakeholder Engagement: Clearly present AI methodologies, performance results, and technical trade-offs to non-technical stakeholders and leadership.
Model Optimization: Implement prompt engineering, adversarial testing, and model optimization strategies to ensure high-quality, efficient, and safe outputs.
Stay Updated: Actively keep up with the latest advancements in GenAI research and incorporate them into our production systems.
Team and project Coordination: Define project scope, timelines, deliverables, success metrics, co-ordinate with and guide offshore technical team on project deliverables.
Required Skills & Qualifications
Experience: 10+ years of experience as an ML Engineer, with at least 1-2 years dedicated to Generative AI or NLP projects, and good experience on AWS cloud platform.
Technical Expertise: Strong proficiency in Python, and IDEs such as Cursor/AWS Kiro, deep learning frameworks (PyTorch or TensorFlow or), utilizing GitHub co-pilot etc.
GenAI Proficiency: Hands-on experience with LLMs (e.g., GPT-4, Llama), RAG architectures, LangChain, Vector Databases, Knowledge graphs, and Agentic AI
MLOps and LLM Ops: Familiarity with Docker, Kubernetes, and CI/CD tools for ML.
AWS Skills: S3, Lambda, Glue, AWS Sage maker, and AWS Bedrock platform
Communication Skills: Excellent verbal and written communication skills; ability to articulate complex technical concepts simply.
Stakeholder Management: Able to collaborate with key business/client stakeholders and manage their expectations
Problem-Solving: Proven ability to work independently in a fast-paced environment and troubleshoot issues.
Preferred Qualifications
Engineering degree in computer science or equivalent, and relevant certification in Machine learning
Experience in banking or financial services domain Payments industry.
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