Senior AI Engineer

Senior AI Engineer

Job Description

We are seeking a highly skilled and experienced Senior AI Engineer to join our dynamic team. The ideal candidate will excel at identifying and articulating complex business problems, and will develop innovative, scalable, and robust AI/ML solutions to address these challenges. Responsibilities will include designing, building, and deploying enterprise-grade AI systems, specifically focused on:

• Agentic AI solutions to automate operational processes (e.g., interpreting trouble tickets, performing basic troubleshooting, interacting with online portals, data entry).

• Retrieval-Augmented Generation (RAG) and ColBERTv2 pipelines for parsing, indexing, and querying enterprise documents to facilitate answers related to process guidelines, product knowledge, and training materials.

• Function calling solutions leveraging Large Language Models (LLMs) to automate and perform precise actions in enterprise workflows.

• Developing and applying reinforcement learning strategies to optimize and automate decision-making processes within enterprise operations.

This role requires hands-on expertise with model fine-tuning, training pipelines, post-training optimization techniques (e.g., model distillation), classification models, and integrating AI systems within complex enterprise environments.

 

Duties and Responsibilities

• Develop and implement AI solutions leveraging fine-tuned Large Language Models (e.g., OpenAI models, LLaMA, Mistral).
• Design, develop, and optimize Retrieval-Augmented Generation (RAG) pipelines using advanced vector databases (e.g., FAISS, Pinecone, Milvus).
• Build and enhance agentic AI systems utilizing frameworks like LangChain, AutoGPT, or similar automation frameworks.
• Deploy scalable ColBERTv2 architectures for semantic retrieval and classification.
• Create robust pre-processing and post-processing pipelines to enhance model performance, accuracy, and interpretability.
• Collaborate closely with cross-functional teams, including product managers, business stakeholders, data scientists, and software engineers.
• Implement best practices in model distillation, quantization, and optimization for deployment in production environments.
• Ensure compliance with enterprise-grade security, privacy standards, and data governance practices.
• Provide leadership and mentorship to team members, supporting their technical development and career growth through coaching, training, and performance feedback.
• Drive timely and successful completion of AI/ML projects by setting clear milestones, tracking progress, removing blockers, and aligning resources

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Machine Learning, AI, or related fields; advanced degree strongly preferred.

• 5+ years of proven experience developing and deploying production-grade AI/ML systems.

• Strong programming skills in Python, familiarity with libraries/frameworks such as PyTorch, TensorFlow, Hugging Face, and LangChain.

• Demonstrated expertise with LLM fine-tuning (e.g., LoRA, PEFT), distillation, and model optimization.

• Practical experience implementing RAG pipelines with embedding technologies and vector stores (e.g., FAISS, Pinecone).

• Proven track record building agentic AI systems capable of interacting with multiple enterprise applications and platforms.

• Solid understanding of NLP techniques, Transformer architectures, semantic search, and document retrieval technologies (e.g., ColBERT).

• Hands-on experience with reinforcement learning techniques, including designing, training, and deploying reinforcement learning models.

Preferred Qualifications:

• Master’s or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or related field.

• Familiarity with cloud-based AI services (e.g., AWS SageMaker, Azure ML, Google Vertex AI).

• Experience with containerization (Docker, Kubernetes) and deployment pipelines (CI/CD).

• Knowledge of advanced AI frameworks and model inference engines such as Triton Inference Server, TensorRT, and ONNX.

• Familiarity with model monitoring, observability tools, and techn