GenAI / AI-ML Engineer
At a glance
Mid-level Machine Learning Engineering role at Two95 International Inc. Gurugram · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and ship scalable AI, machine-learning and GenAI applications, including production RAG pipelines and LLM tools
- Create agent-based and multi-agent workflows with prompt design, LangGraph and related frameworks
- Train, tune, assess and monitor models, while developing data preparation and feature-engineering pipelines
- Build Python REST APIs and backend services, then resolve issues across models, data pipelines and production performance
- Connect structured and unstructured sources for semantic search and document retrieval, and assess quality with relevant metrics
What you bring
- You have four to six years of professional experience, including at least one year on GenAI or LLM projects
- You bring hands-on Python, SQL and REST API development experience, plus practical knowledge of machine-learning and deep-learning methods
- You have built RAG architectures and agentic applications, and worked with LangChain, LangGraph or another listed tool
- You have used vector search, AWS AI services and deployment tools such as Docker and CI/CD pipelines
Who this fits
This mid-level role suits an engineer who can build and operate GenAI systems, APIs and machine-learning models. You must be based in Noida, Gurugram, Delhi or another NCR location, join immediately and work in a hybrid setup from the Gurugram office. Interviews are scheduled for August 1 or August 3, 2026.
From the employer
Role Overview
We are looking for an experienced GenAI / AI-ML Engineer with strong hands-on expertise in Python, machine learning, deep learning, Large Language Models, Retrieval-Augmented Generation, and agentic AI systems. The selected candidate will be responsible for designing, developing, and deploying scalable AI-powered applications. The role requires practical experience in building production-ready RAG pipelines, LLM-powered applications, REST APIs, machine-learning models, and cloud-based AI solutions using AWS.
Key Responsibilities
● Design, develop, test, and deploy scalable AI, machine-learning, deep-learning, and Generative AI solutions. ● Build and optimise Retrieval-Augmented Generation pipelines using modern frameworks, embedding models, and vector databases. ● Develop LLM-powered applications using prompt engineering, AI agents, LangGraph, and multi-agent workflows. ● Fine-tune, evaluate, deploy, and monitor machine-learning and deep-learning models. ● Build REST APIs and backend services for AI applications using FastAPI or similar frameworks. ● Design data-preprocessing, feature-engineering, model-training, and model-evaluation pipelines. ● Integrate structured and unstructured data sources to deliver accurate and context-aware AI solutions. ● Implement semantic search and document-retrieval architectures. ● Evaluate RAG and Generative AI solutions using appropriate quality and performance metrics. ● Collaborate with Data Engineering, DevOps, Product, and other cross-functional teams. ● Ensure the scalability, reliability, security, and performance of AI applications in production environments. ● Follow software-engineering best practices, coding standards, version-control processes, and Agile methodologies. ● Troubleshoot model, API, data-pipeline, and production-performance issues.
Requirements
Mandatory Skills
Programming and Backend Development ● Strong hands-on experience in Python. ● Strong working knowledge of SQL. ● Experience developing REST APIs using FastAPI or similar Python frameworks. ● Good understanding of object-oriented programming, modular development, testing, and software-engineering best practices. ● Experience working in Agile development environments. Machine Learning and Deep Learning Hands-on experience with: ● Scikit-learn ● TensorFlow ● PyTorch ● Keras Strong understanding of: ● Regression ● Classification ● Clustering ● Feature engineering ● Data preprocessing ● Model evaluation ● Hyperparameter tuning ● Model deployment and monitoring
NLP and Generative AI
● Minimum one year of hands-on experience working on GenAI or LLM-based projects. ● Strong understanding of Large Language Models and Natural Language Processing concepts. ● Experience in prompt engineering and prompt optimisation. ● Hands-on experience designing and implementing RAG architectures. ● Experience building agentic AI or multi-agent applications. ● Practical experience with: ○ LangChain ○ LangGraph ○ OpenAI APIs ○ Hugging Face ○ LangSmith
RAG and Vector Databases
● Experience working with vector databases and similarity-search technologies, including: ○ Pinecone ○ FAISS ● Knowledge of embedding models, chunking strategies, semantic search, document retrieval, and reranking. ● Experience evaluating RAG solutions using metrics or frameworks such as: ○ RAGAS ○ BLEU ○ ROUGE
AWS and DevOps
Hands-on experience with AWS services such as: ● Amazon EC2● Amazon S3 ● Amazon SageMaker ● Amazon Bedrock
Experience working with:
● Docker ● Git ● JIRA ● CI/CD pipelines
Preferred Qualifications
● Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline. ● Experience deploying AI and LLM applications in production environments. ● Understanding of LLM observability, hallucination control, guardrails, latency optimisation, and cost optimisation. ● Experience integrating enterprise data sources with GenAI applications. ● Strong analytical, problem-solving, communication, and stakeholder-management skills.
Candidate Eligibility
● Candidates must have 4–6 years of overall professional experience. ● At least one year of practical GenAI or LLM project experience is mandatory. ● Candidates must be immediate joiners. ● Candidates should be based in Noida, Gurugram, Delhi, or another NCR location. ● Candidates must be comfortable working in a hybrid model from the Gurugram office. ● Candidates should be available for an interview on August 1 or August 3, 2026.