AI/ML Engineer

Overview of 66degrees

66degrees is an end-to-end AI transformation partner that guides enterprises from complex business challenges to clear, quantifiable outcomes. Our company is the culmination of several successful firms, each a leader in its own right in cloud, artificial intelligence, and data. This convergence of talent and expertise is how we help businesses reach their own "inflection point," where chaotic data becomes a strategic asset, complexity becomes clarity, and AI becomes an engine for growth. Our ultimate vision is to be the catalyst for a future where every business operates as an intelligent enterprise, with autonomous systems unlocking human potential.

At 66degrees, we believe in thriving through challenges and winning together. These values not only guide us in achieving our goals as a company but also for our people. We are dedicated to creating a significant impact for our employees by fostering a culture that sparks innovation and supports professional and personal growth along the way. 

Overview of Role

We are seeking a highly skilled and motivated AI Engineer with 5 to 8 years of experience to join our team. This role is a unique blend of deep technical expertise and strategic customer engagement. The ideal candidate will have a strong background in Python, Generative AI, and the end-to-end MLOps lifecycle.

As an AI Engineer, you will be responsible for designing, building, and deploying sophisticated AI solutions, with a focus on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). You will partner directly with business stakeholders and customers to understand their needs, translate them into technical requirements, and deliver robust, scalable AI applications that drive significant business impact.

Responsibilities:

• End-to-End Solution Development: Partner with internal and external stakeholders to lead the full lifecycle of AI projects—from use case scoping and data exploration to model development, deployment, and performance optimization.
• Data-Centric AI Implementation: Champion a data-first approach to building AI applications. Design and implement scalable text processing pipelines, and establish best practices for data quality, including curating vector database collections, and developing tailored data chunking and vectorization strategies.
• Generative AI System Architecture: Architect and implement advanced AI systems utilizing modern orchestration frameworks (e.g., LangChain, LlamaIndex). Your expertise will cover the core components of the Gen AI stack:

• Retrieval: Implementing efficient and relevant information retrieval.
• Augmentation & Generation (RAG): Building robust RAG pipelines.
• Fine-Tuning: Fine-tuning embedding and generative models for specific tasks.
• Evaluation: Establishing metrics and frameworks to evaluate model performance.

• LLMOps & Production Deployment: Manage the deployment of AI models as scalable inference endpoints. Implement and maintain LLMOps best practices, including CI/CD for models, observability, tracing, and version control using Git.
• Stakeholder Management & Enablement: Forge and manage strong relationships with customer leadership. Serve as the primary technical voice, educating stakeholders on the capabilities and limitations of AI, and conducting workshops to transfer knowledge and ensure successful adoption.

Skills and Qualifications:

• 5+ years of customer-facing experience in the design and implementation of AI/ML solutions.
• Proficient in Python and SQL.
• Expertise in AI frameworks and libraries, such as scikit-learn, Hugging Face Transformers, LangChain/Graph, GCP, and OpenAI APIs.
• Experience leading strategic, customer-facing initiatives and collaborating with business stakeholders to ensure ML solutions drive successful business outcomes, with a strong focus on teaching and enablement.
• Outstanding presentation skills to technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos.
• Ability to work in a fast-paced environment and balance priorities across multiple projects at once.
• B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience.
• Excellent communication skills in English