Job Role:
We are seeking a hands-on AI Engineer (or a Data Scientist with strong AI engineering
experience) to take ownership of our production-grade Generative AI ecosystem. You will
build, deploy, and maintain advanced LLM workflows focusing on RAG and agentic routing
that integrate directly into live enterprise applications to deliver actionable business insights.
Architect & Build: Design and maintain scalable Retrieval-Augmented Generation
(RAG) pipelines and smart routing mechanisms to optimize how LLMs process
varying business queries.
Deploy & Automate: Design CI/CD pipelines to automate the deployment of model
updates, prompts, and backend logic with zero downtime.
Integrate & Connect: Seamlessly link our AI workflows into enterprise software
products by integrating with existing backend APIs and data services.
Monitor & Debug: Actively trace system health in production, pinpointing and
resolving latency bottlenecks, retrieval failures, or prompt inefficiencies.
Collaborate: Work directly with project leadership to translate complex business
requirements into technical AI features.
Core Competencies:
Experience: Experience as an AI Engineer, or as a Data Scientist with strong
applied AI engineering experience. Must have advanced Python and strong SQL
programming skills for data extraction and pipeline development.
Prompt Engineering: Proven expertise in designing, testing, and optimizing
complex prompts for various LLMs to ensure high accuracy, grounded responses,
and cost-efficiency.
GenAI Orchestration: Deep practical experience building complex workflows with
LangChain.
Vector Infrastructure: Production-level experience managing and querying vector
databases (specifically ChromaDB, or similar like Pinecone/Milvus).
Observability: Hands-on experience debugging and tracing live AI applications
using Langfuse (or equivalent tracking tools).
Cloud & MLOps:
o Proficiency in at least one major cloud platform (Azure, AWS, or GCP).
o Experience with CI/CD tools (e.g., GitHub Actions, GitLab CI, Azure DevOps).
o Familiarity with containerization (Docker) and RESTful API integration.
Added Advantage
Experience building autonomous agents with frameworks like LangGraph or CrewAI.
Familiarity with AI evaluation frameworks (e.g., RAGAS) or model fine-tuning (PEFT/LoRA).
Prior exposure to life sciences, pharma, or healthcare data.
