Gen AI Specialist

Job Title: AI/ML Engineer – Generative AI Specialist

Experience: 4–6 Years

Location: Pune, Indore.

About the Role:

We are seeking a highly skilled AI/ML Engineer with a solid foundation in Machine Learning

and deep hands-on experience in Generative AI (GenAI). The ideal candidate will have strong

capabilities in building, training, and deploying ML models, along with significant experience

working with frameworks such as LangChain, LangGraph, and platforms like Hugging Face,

vector databases, and various LLMs. You’ll be a key contributor in developing smart

assistants, AI agents, and ML solutions that solve complex business problems.

Key Responsibilities:

 Design, train, evaluate, and deploy traditional ML models as well as Generative AI-

based applications.

 Work on supervised, unsupervised, and deep learning models including regression,

classification, clustering, and sequence models.

 Build end-to-end ML pipelines including data preprocessing, feature engineering,

model training, hyperparameter tuning, and evaluation.

 Develop and optimize LLM-based workflows using LangChain, LangGraph, and

orchestration frameworks.

 Fine-tune, evaluate, and integrate LLMs such as GPT, LLaMA, Claude, Mistral, Falcon,

Cohere, and Gemini.

 Good exposure on using Azure Open AI and hosting applications in Azure

environment

 Implement Retrieval-Augmented Generation (RAG) using embeddings and vector

stores like FAISS, Pinecone, or Chroma.

 Apply prompt engineering, LoRA, PEFT, and adapter-based fine-tuning to optimize

LLMs for specific tasks.

 Build Agentic AI systems with tool-use capabilities and reasoning chains (e.g., ReAct,

AutoGPT, BabyAGI, CrewAI).

 Use Hugging Face for leveraging pre-trained models and datasets for rapid

experimentation.

 Collaborate with product, data, and engineering teams to productionize AI solutions

using scalable cloud infrastructure.

Required Skills & Qualifications:

 4–6 years of experience in AI/ML, with at least 2 years in Machine Learning and

Generative AI projects.

Agentic AI hands on is required to have a strong working understanding for the same.

 Strong grasp of core ML concepts such as model selection, evaluation metrics,

bias/variance tradeoff, overfitting/underfitting, etc.

 Hands-on experience in building and tuning models using scikit-learn, XGBoost,

LightGBM, TensorFlow, or PyTorch.

 Proven experience in building ML pipelines with feature engineering, model tuning,

cross-validation, and A/B testing.

 Proficiency in LangChain, LangGraph, and integrating with Hugging Face Transformers

ecosystem.

 Deep knowledge of various LLMs and techniques like prompt engineering, few-shot

learning, and instruction tuning.

 Must-have experience in building Agentic AI systems and coordinating multi-agent

flows or tool-chaining.

 Familiarity with LLMOps/MLOps tools (e.g., MLflow, Weights & Biases, Kubeflow,

SageMaker, or Vertex AI).

 Strong programming skills in Python, and experience deploying models using FastAPI,

Flask, or Streamlit.

 Working experience with cloud platforms (AWS/GCP/Azure) and handling GPU/TPU

resources.

 Solid understanding of data structures, algorithms, and software engineering best

practices.

Good to Have:

 Experience with LangSmith, PromptLayer, or other LLM observability tools

 Familiarity with Guardrails.AI, semantic caching, and output validation techniques

 Exposure to multi-modal models like CLIP, DALL·E, Stable Diffusion, or Whisper

 Contributions to open-source GenAI or ML libraries/projects

 Domain expertise in areas like healthcare, finance, manufacturing, or legal tech