AI/ML Engineer

We are seeking a forward-leaning AI/ML Engineer or Lead with deep technical expertise in Generative AI (GenAI), Large Language Models (LLMs), and modern machine learning infrastructure. This role will design, develop, and optimize AI solutions that directly drive our strategic product offerings and internal automation initiatives. If you thrive in an environment where innovation is expected and impact is non-negotiable, you re the person we need.

Key Responsibilities

• LLM Development & Fine-Tuning

  • Design, customize, and fine-tune LLMs (e.g., GPT, LLaMA, Mistral) for domain-specific use cases.

  • Evaluate trade-offs between open-source and closed-source models based on performance, scalability, and IP risk.

• GenAI Solutions Engineering

  • Architect end-to-end Generative AI pipelines across NLP, document processing, conversational AI, and retrieval-augmented generation (RAG).

  • Build GenAI prototypes and drive their evolution into scalable production systems.

• Machine Learning & MLOps

  • Develop supervised and unsupervised ML models using Python frameworks (e.g., scikit-learn, PyTorch, TensorFlow).

  • Implement and maintain MLOps pipelines for CI/CD, model monitoring, and lifecycle management on AWS (SageMaker, Lambda, S3, etc.).

• Data Infrastructure & Pipelines

  • Design scalable data ingestion, transformation, and feature engineering pipelines leveraging SQL and AWS services (Glue, Redshift, Athena).

  • Ensure data quality, reproducibility, and governance across all ML systems.

• Leadership (for Lead-level candidates)

  • Provide technical mentorship and architectural guidance to a team of junior/mid-level engineers.

  • Influence roadmap planning and cross-functional prioritization with Product and Engineering stakeholders.

Required Qualifications

• 5+ years (Engineer) or 7+ years (Lead) experience in applied ML, with 2+ years in GenAI and LLMs.

• Proficient in Python and related ML frameworks (e.g., PyTorch, HuggingFace Transformers, LangChain).

• Proven hands-on experience with AWS cloud stack: EC2, S3, SageMaker, Lambda, ECS.

• Advanced SQL skills with experience in high-volume structured/unstructured data environments.

• Deep understanding of LLM training/fine-tuning, prompt engineering, and vector search.

• Experience with production-grade ML lifecycle management (e.g., MLflow, Kubeflow, Airflow).

• Strong grasp of software engineering best practices (CI/CD, version control, testing).

Preferred Qualifications

• Experience with RAG, embeddings, FAISS, Weaviate, or Pinecone.

• Exposure to multi-modal models (text+vision/speech).

• Familiarity with data privacy, AI model governance, and ethical ML practices.

• Publications or contributions to open-source LLM or GenAI initiatives is a strong plus.

• Past experience in a start-up or high-growth environment is desirable.