Artificial Intelligence Engineer

ANAROCK; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

ANAROCK in Bengaluru seeks a seasoned AI Engineer to design, fine-tune, and deploy scalable AI features at production scale. You will work at the intersection of LLMs, ML, and software engineering to build production-ready AI pipelines that power our core product.

You will implement RAG architectures, optimize embeddings, and refine prompts. Responsibilities include deploying models on cloud platforms, integrating LLM APIs, building evaluation pipelines, and collaborating with architects,

Qualifications

• 5-7 years of AI/ML experience with production systems.
• Strong Python skills and deep knowledge of AI/ML libraries.
• Hands-on experience with LLM APIs and prompt engineering techniques.
• Experience with RAG systems, embedding models, and vector stores.
• Familiarity with LangChain, LlamaIndex, AutoGen or Semantic Kernel.
• Knowledge of LoRA/QLoRA/PEFT and fine-tuning methods.
• Experience deploying models on cloud platforms (AWS/GCP/Azure).
• Understanding of data preprocessing, feature engineering, and metrics.
• Proficiency with MLOps tools and container orchestration.

Responsibilities

• Design, develop, and deploy AI/ML models and pipelines in production environments
• Implement Retrieval-Augmented Generation (RAG) architectures and agentic AI workflows
• Fine-tune and optimize LLMs for domain-specific use cases using RLHF, LoRA, QLoRA
• Build robust prompt engineering frameworks and evaluation pipelines
• Integrate LLM APIs (OpenAI, Claude, Gemini) and open-source models into product features
• Develop and maintain vector search infrastructure and embedding pipelines
• Collaborate with architects, backend engineers, and product teams on AI feature delivery
• Monitor model performance, conduct A/B testing, and iterate based on metrics
• Implement guardrails, safety layers, and hallucination-mitigation strategies

Skills

Python

AI/ML libraries

LLM APIs

Prompt engineering

RAG systems

Embedding models

LangChain

LoRA/QLoRA/PEFT

Cloud deployment

MLOps

Jupyter/FastAPI/Streamlit

Debugging

Tools

PyTorch

TensorFlow

HuggingFace

OpenAI API

Cohere

text-embedding-3

BGE

Kubernetes

Docker

MLflow

DVC

Weights & Biases

BentoML

Vertex AI

SageMaker

Job description

We are seeking a seasoned AI Engineer to build, fine-tune, and deploy intelligent AI systems at scale. You will work at the intersection of LLMs, machine learning, and software engineering — developing production-ready AI features and pipelines that power our core product.

Responsibilities

• Design, develop, and deploy AI/ML models and pipelines in production environments
• Implement Retrieval-Augmented Generation (RAG) architectures and agentic AI workflows
• Fine-tune and optimize LLMs for domain-specific use cases using RLHF, LoRA, QLoRA
• Build robust prompt engineering frameworks and evaluation pipelines
• Integrate LLM APIs (OpenAI, Claude, Gemini) and open-source models into product features
• Develop and maintain vector search infrastructure and embedding pipelines
• Collaborate with architects, backend engineers, and product teams on AI feature delivery
• Monitor model performance, conduct A/B testing, and iterate based on metrics
• Implement guardrails, safety layers, and hallucination-mitigation strategies

Qualifications

5-7 years exp preferable

Required Skills

• Strong expertise in Python, with deep knowledge of AI/ML libraries (PyTorch, TensorFlow, HuggingFace Transformers)
• Hands-on experience with LLM APIs and prompt engineering techniques (CoT, few-shot, ReAct)
• Experience with RAG systems, embedding models (text-embedding-3, BGE, Cohere), and vector stores
• Knowledge of agentic frameworks: LangChain, LlamaIndex, AutoGen, CrewAI, or Semantic Kernel
• Familiarity with fine-tuning techniques: LoRA, QLoRA, PEFT, instruction tuning
• Experience deploying models on cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
• Understanding of data preprocessing, feature engineering, and model evaluation metrics
• Proficiency with MLOps tools: MLflow, DVC, Weights & Biases, BentoML
• Experience with containerization and orchestration: Docker, Kubernetes
• Strong debugging and experimentation skills with Jupyter, FastAPI, Streamlit

Preferred Skills

• Experience with multi-modal models (vision-language models, Whisper, DALL-E)
• Published papers or Kaggle/competition achievements
• Exposure to speech AI, computer vision, or NLP specializations
• Knowledge of responsible AI, fairness metrics, and bias

Pay range and compensation package

As per market standards and experience of candidate (we are looking for right talent who is committed to Delivering results)

Job Type:

Full Time

Office Location:

Equal Opportunity Statement

We are committed to diversity and inclusivity.