Lead Software Engineer

We are seeking a Data Scientist with experience to design, build, and deploy GenAI and LLM-powered solutions. The role combines applied ML, prompt engineering, and MLOps to deliver scalable, production-grade AI systems.

Key Responsibilities

Develop and deploy LLM-based applications (RAG, chatbots, copilots, summarization, search)

Fine-tune, evaluate, and optimize LLMs and transformer models

Design prompt engineering strategies and guardrails for safe, reliable outputs

Build retrieval pipelines using vector databases (FAISS, Pinecone, etc.)

Implement end-to-end ML pipelines (data model deployment monitoring)

Collaborate with engineering teams to integrate models via APIs and microservices

Monitor model performance, drift, and costs; implement feedback loops

Ensure responsible AI practices (bias mitigation, explainability, governance)

Software Engineering, Data Science, Technical/Engineering Design, Data Modeling, Application Programming Interface (API), Microservices, Performance Modeling, Performance Analysis, Artificial Intelligence (AI), Python Programming/Scripting Language, 3-6 years of experience in Data Science / ML Engineering / Applied AI

Strong programming in Python

Hands-on experience with LLMs (OpenAI, Azure OpenAI, etc.)

Experience with transformers, embeddings, and NLP pipelines

Familiarity with RAG architectures and vector search

Solid foundation in ML algorithms, statistics, and evaluation metrics

Strong SQL and data handling skills

MLOps & Engineering Skills

Experience with model deployment (FastAPI, Flask, Docker, Kubernetes)

Exposure to CI/CD pipelines for ML (GitHub Actions, Azure ML, SageMaker, etc.)

Experience in model monitoring, logging, and versioning (MLflow, Weights & Biases)

Knowledge of data pipelines (Airflow, Spark)

Familiarity with cloud platforms (AWS, Azure, or GCP)

Preferred Qualifications

Experience fine-tuning LLMs (LoRA, PEFT techniques)

Exposure to multi-modal models (text + image/audio)

Knowledge of AI safety, hallucination mitigation, and evaluation frameworks

Domain experience in enterprise use cases (support, search, analytics, automation)

Soft Skills

Strong problem-solving and experimental mindset

Ability to translate ambiguous business problems into AI solutions

Excellent communication with technical & non-technical stakeholders

Education

Bachelor’s or Master’s in Computer Science, AI, Data Science, Statistics, or a related field