AI Engineer

Elowit Technologies; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Elowit Technologies in Bengaluru seeks an AI Engineer to bridge ML research and production software. You will design, build, and deploy AI-powered features addressing complex problems. 3–6 years of experience with a deep AI lifecycle understanding is expected.

You will architect scalable AI solutions, optimize models, build data pipelines, and ensure rigorous evaluation and safe deployment through MLOps. You will also mentor junior engineers and contribute to architectural discussions.

Qualifications

• 3–6 years of experience in AI/ML engineering.
• Strong understanding of the AI lifecycle from data curation to deployment.
• Experience with model evaluation, safety, and reducing hallucinations.

Responsibilities

• Architect & Build scalable AI solutions, including RAG pipelines, agentic workflows, and predictive models.
• Model Optimization: Fine-tune Open Source LLMs and integrate with proprietary models.
• Data Engineering: Build robust data pipelines to preprocess unstructured data for training and evaluation.
• Evaluation & Testing: Establish evaluation frameworks to ensure accuracy and safety.
• Deployment (MLOps): Containerize and deploy models using Docker/Kubernetes and monitor in production.
• Mentorship: Guide junior engineers and participate in code reviews.

Skills

MLOps

Docker

Kubernetes

LLMs

Tools

Open Source LLMs

Job description

Role Overview

• We are seeking a skilled AI Engineer to bridge the gap between machine learning research and production-grade software.
• You will be responsible for designing, building, and deploying AI-powered features that solve complex problems.
• With 3-6 years of experience, you are expected to have a deep understanding of the AI lifecyclefrom data curation and model selection to fine-tuning and MLOps.

Key Responsibilities

• Architect & Build: Design and implement scalable AI solutions, including RAG (Retrieval-Augmented Generation) pipelines, agentic workflows, and predictive models.
• Model Optimization: Fine-tune Open Source LLMs (like Llama, Mistral) and manage integration with proprietary models (GPT-4, Claude, Gemini).
• Data Engineering: Build robust data pipelines to preprocess unstructured data for training and evaluation.
• Evaluation & Testing: Establish rigorous evaluation frameworks (LLM-as-a-judge, G-Eval, etc.) to ensure model accuracy, safety, and reduced hallucination.
• Deployment (MLOps): Containerize and deploy models using Docker/Kubernetes and manage model monitoring in production.
• Mentorship: Provide technical guidance to junior engineers and contribute to code reviews and architectural discussions.