Fractal Analytics

Role description:

You will be building curated enterprise grade solutions for GenAI application deployment at a production scale for clients. Solid understanding and hands on skills for GenAI application deployment that includes development and engineering skills. The role requires development and engineering skills on GenAI application development including data ingestion, choosing the right fit LLMs, simple and advanced RAG, guardrails, prompt engineering for optimisation, traceability, security, LLM evaluation, observability, and deployment at scale on cloud or on premise. As this space evolves very rapidly, candidates must also demonstrate knowledge on agentic AI frameworks. Candidates having strong background on ML with engineering skills is highly preferred for LLMOps role.

Required skills:

• 4-8 years of experience in working on ML projects that includes business requirement gathering, model development, training, deployment at scale and monitoring model performance for production use cases
• Strong knowledge on Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, AgentOps (optional)
• Should have worked on proprietary and open source large language models
• Experience on LLM fine tuning, creating distilled model from hosted LLMs
• Building data pipelines for model training
• Experience on model performance tuning, RAG, guardrails, prompt engineering, evaluation and observability
• Experience in GenAI application deployment on cloud and on-premise at scale for production
• Experience in creating CI/CD pipelines
• Working knowledge on Kubernetes
• Experience in minimum one cloud: AWS / GCP / Azure to deploy AI services
• Experience in creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen
• Experience in light weight UI development using streamlit or chainlit (optional)
• Desired experience on open-source tools for ML development, deployment, observability and integration
• Background on DevOps and MLOps will be a plus
• Experience working on collaborative code versioning tools like GitHub/GitLab
• Team player with good communication and presentation skills

Responsibilities

• Design and implement advanced solutions utilizing Large Language Models (LLMs).
• Demonstrate self-driven initiative by taking ownership and creating end-to-end solutions.
• Conduct research and stay informed about the latest developments in generative AI and LLMs.
• Develop and maintain code libraries, tools, and frameworks to support generative AI development.
• Participate in code reviews and contribute to maintaining high code quality standards.
• Engage in the entire software development lifecycle, from design and testing to deployment and maintenance.
• Collaborate closely with cross-functional teams to align messaging, contribute to roadmaps, and integrate software into different repositories for core system compatibility.
• Possess strong analytical and problem-solving skills.
• Demonstrate excellent communication skills and the ability to work effectively in a team environment.

Primary Skills

• Generative AI: Proficiency with SaaS LLMs, including Lang chain, llama index, vector databases, Prompt engineering (COT, TOT, ReAct, agents). Experience with Azure OpenAI, Google Vertex AI, AWS Bedrock for text/audio/image/video modalities.
• Familiarity with Open-source LLMs, including tools like TensorFlow/Pytorch and Huggingface. Techniques such as quantization, LLM finetuning using PEFT, RLHF, data annotation workflow, and GPU utilization.
• Cloud: Hands-on experience with cloud platforms such as Azure, AWS, and GCP. Cloud certification is preferred.
• Application Development: Proficiency in Python, Docker, FastAPI/Django/Flask, and Git.
• Natural Language Processing (NLP): Hands-on experience in use case classification, topic modeling, Q&A and chatbots, search, Document AI, summarization, and content generation.
• Computer Vision and Audio: Hands-on experience in image classification, object detection, segmentation, image generation, audio, and video analysis.