Prismforce Pvt Ltd - AI Engineer - LLM/RAG

Prismforce; Maharashtra; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Prismforce in Maharashtra is hiring a skilled AI engineer to design, develop, and deploy AI-driven applications and intelligent software solutions.

You will build and optimize ML/DL models for production, work with LLMs, NLP, and RAG pipelines, and craft RESTful APIs for enterprise integration. The role requires collaboration with data scientists, software engineers, and product managers to deliver scalable AI services.

Qualifications

• Proficient in Python with experience in ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Hands-on with LLMs, Generative AI, NLP and RAG-based applications.
• Experience building REST APIs using FastAPI or Flask.
• Familiarity with Docker, Kubernetes and cloud platforms (AWS/Azure/GCP).
• Strong problem-solving and collaboration in cross-functional product teams.

Responsibilities

• Design, develop, and deploy AI-driven applications and services.
• Build ML/DL models for production and optimize for latency.
• Collaborate with data scientists and software engineers across the lifecycle.
• Implement MLOps for training, deployment, monitoring, and version control.
• Evaluate new AI technologies and ensure governance and security.

Skills

Python

PyTorch

TensorFlow

NLP

RAG

LLMs

REST APIs

FastAPI/Flask

Git

Tools

Docker

Kubernetes

LangChain

LlamaIndex

Pinecone

Job description

• Design, develop, and deploy AI-driven applications and intelligent software solutions.
• Build and optimize machine learning and deep learning models for production environments.
• Develop applications leveraging Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP).
• Fine-tune foundation models using domain-specific datasets and implement Retrieval-Augmented Generation (RAG) pipelines.
• Develop AI services and RESTful APIs for seamless integration with enterprise applications.
• Build scalable backend systems to support AI inference and model serving.
• Design data pipelines for model training, evaluation, and continuous improvement.
• Implement prompt engineering techniques to improve LLM performance and response quality.
• Optimize model accuracy, latency, scalability, and infrastructure utilization.
• Collaborate with software engineers, product managers, and data scientists throughout the product lifecycle.
• Deploy AI models using containerization and orchestration technologies.
• Implement MLOps practices for automated model training, deployment, monitoring, and version control.
• Evaluate emerging AI technologies and recommend suitable solutions for business use cases.
• Ensure AI applications meet security, governance, and responsible AI standards.
• Troubleshoot production issues and continuously improve system performance.

Key Responsibilities

• Design, develop, and deploy AI-driven applications and intelligent software solutions.
• Build and optimize machine learning and deep learning models for production environments.
• Develop applications leveraging Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP).
• Fine-tune foundation models using domain-specific datasets and implement Retrieval-Augmented Generation (RAG) pipelines.
• Develop AI services and RESTful APIs for seamless integration with enterprise applications.
• Build scalable backend systems to support AI inference and model serving.
• Design data pipelines for model training, evaluation, and continuous improvement.
• Implement prompt engineering techniques to improve LLM performance and response quality.
• Optimize model accuracy, latency, scalability, and infrastructure utilization.
• Collaborate with software engineers, product managers, and data scientists throughout the product lifecycle.
• Deploy AI models using containerization and orchestration technologies.
• Implement MLOps practices for automated model training, deployment, monitoring, and version control.
• Evaluate emerging AI technologies and recommend suitable solutions for business use cases.
• Ensure AI applications meet security, governance, and responsible AI standards.
• Troubleshoot production issues and continuously improve system performance.

Required Skills

• Strong programming skills in Python.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Hands-on experience with Large Language Models (LLMs), and Generative AI technologies.
• Strong understanding of NLP, Transformers, Embeddings, and Vector Databases.
• Experience building RAG-based applications.
• Knowledge of prompt engineering and LLM optimization techniques.
• Experience with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks.
• Experience developing REST APIs using FastAPI or Flask.
• Strong understanding of data structures, algorithms, and software engineering principles.
• Experience with SQL and NoSQL databases.
• Familiarity with Docker, Kubernetes, and containerized deployments.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Knowledge of CI/CD pipelines and MLOps tools.
• Familiarity with Git and Agile development methodologies.

Preferred Qualifications

• Experience working with OpenAI, Anthropic, Google Gemini, or open-source LLMs such as Llama, Mistral, or Qwen.
• Exposure to AI agents, multi-agent systems, and autonomous workflows.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, or ChromaDB.
• Knowledge of distributed computing, GPU optimization, and model serving frameworks.
• Experience with monitoring and observability tools for AI applications.
• Understanding of responsible AI, model governance, and AI security best practices.

Soft Skills

• Strong analytical and problem-solving abilities.
• Excellent communication and collaboration skills.
• Ability to work independently and in cross-functional teams.
• Passion for innovation and continuous learning in AI technologies.
• Strong ownership, accountability, and attention to detail.
• Ability to thrive in a fast-paced product development environment.

(ref:hirist.tech)