Senior AI/ML Specialist

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Generative AI Lead Developer

The Generative AI Lead Developer will be responsible for designing, building, and deploying cutting-edge generative AI applications and services. This role involves hands-on development of LLM-powered solutions such as copilots, virtual assistants, and document intelligence tools, while also leading a team of developers in implementing scalable, secure, and compliant GenAI systems.

You’ll collaborate closely with AI architects, data scientists, and business product owners to bring generative AI use cases from prototype to production, ensuring high performance, safety, and business alignment..

Develop and deploy generative AI applications using LLMs (GPT, Claude, Gemini, Mistral, etc.) Implement Retrieval-Augmented Generation (RAG) pipelines integrating vector databases.
Integrate AI models into enterprise systems via APIs and microservices
Lead a small team of AI developers and ML engineers to deliver high-quality, production-ready GenAI solutions.
Establish coding best practices, CI/CD pipelines, and MLOps integration for model deployment and monitoring.
Conduct code reviews, performance tuning, and prompt optimization.
Partner with the AI Architect to design scalable and reusable components.
Work cross-functionally with business analysts and product teams to translate business needs into technical implementations.
Collaborate with data engineers on data curation, preprocessing, and embedding pipelines.
Evaluate, fine-tune, and deploy large language models (LLMs) and multimodal models.
Implement guardrails for content filtering, prompt injection defense, and output validation.

Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field.

• 6–10 years of total experience, including at least 2–3 years in AI/ML solution development.
• Strong proficiency in Python and experience with AI/ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers).
• Proven hands-on experience developing solutions using LLM APIs (OpenAI, Azure OpenAI, Anthropic, Cohere, etc.).
• Experience with prompt engineering, embeddings, and vector search.
• Solid understanding of MLOps tools and deployment (Docker, Kubernetes, MLflow, Databricks, Azure ML, or AWS SageMaker).

Experience in building conversational AI, copilots, or workflow automation tools.
Experience integrating GenAI with enterprise systems (SharePoint, ServiceNow, SAP, etc.).
Understanding of responsible AI principles — data privacy, model safety, and bias mitigation.
Exposure to front-end frameworks (React, Streamlit, or Gradio) for building interactive AI applications.

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