AI Engineer

If you are looking to excel and make a difference, take a closer look at us…

This posting is used to facilitate Contingent Worker Hiring Process

Key Responsibilities:

1: Core Model Implementation

• LLM Integration: Integrate proprietary (OpenAI, Anthropic, Gemini) and open-source models (Llama 3+, Mistral) into applications.

• Prompt Engineering: Design, test, and version-control advanced prompt templates (Chain-of-Thought, Few-Shot) to improve output reliability.

• Fine-Tuning: Perform parameter-efficient fine-tuning (PEFT) using techniques like LoRA or QLoRA for domain-specific tasks.

2. Systems & Architecture

• RAG Pipelines: Build and optimize RAG systems using vector databases (Pinecone, Weaviate, Milvus) to provide LLMs with proprietary context.

• Agentic Workflows: Develop multi-agent systems using frameworks like LangGraph or CrewAI where models can "reason" and call external tools (APIs, Databases).

• Backend Development: Write clean, scalable Python code (FastAPI/Flask) to serve AI models as microservices.

3. Evaluation & MLOps

• LLM Evaluation: Implement automated testing for hallucinations and bias using frameworks like Ragas or LangSmith.

• Deployment: Containerize applications using Docker and assist in deploying models to cloud environments (AWS SageMaker, Azure AI, or GCP).

• Cost & Latency Optimization: Monitor token usage and implement caching (e.g., GPTCache) to reduce operational costs.

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