Interesting Job Opportunity: AI Engineer - LLM/RAG

Prismforce; Maharashtra; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

Prismforce is seeking an AI/ML Engineer to design, develop, and deploy AI-driven applications and intelligent software solutions. Build and optimize ML/DL models for production environments, and develop applications leveraging LLMs, Generative AI, and NLP.

You will fine-tune foundation models with domain data, implement RAG pipelines, develop REST APIs, containerize models, and apply MLOps for automated training, deployment, monitoring, and version control, while ensuring security and

Qualifications

• Proficient in Python and ML frameworks; hands-on LLMs and NLP experience.
• Experience with RAG-enabled apps and vector databases.
• Experience building REST APIs and MLops workflows.

Responsibilities

• Design, develop, and deploy AI-driven applications and intelligent software solutions.
• Build and optimize ML/DL models for production environments.
• Develop applications leveraging LLMs, Generative AI, and NLP.
• Fine-tune foundation models using domain data and implement RAG pipelines.
• Develop AI services and RESTful APIs for enterprise integration.
• Build scalable backend systems for AI inference and model serving.
• Design data pipelines for training, evaluation, and continuous improvement.
• Implement prompt engineering to improve LLM performance.
• Optimize accuracy, latency, scalability, and infra utilization.
• Collaborate with engineers, PMs, and data scientists across the lifecycle.
• Deploy AI models using containers and orchestration technologies.
• Implement MLOps for automated training, deployment, monitoring, and versioning.
• Evaluate emerging AI tech and recommend business solutions.
• Ensure security, governance, and responsible AI standards.
• Troubleshoot production issues and improve system performance.

Skills

Python

Machine Learning

Deep Learning

LLMs

NLP

RAG

LangChain

REST APIs

FastAPI

Docker

Kubernetes

MLOps

Git

Agile

Tools

LangChain

LangGraph

LlamaIndex

Pinecone

Weaviate

Milvus

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.