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Senior AI/ML Engineer

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Flipped.ai - Transforming Talent Acquisition with AI

Kolkata Full-Time 4–8 yrs exp Posted 1 month ago · Apply by Jul 18, 2026

Job Description

Full Time

Exp : 3- 8 Years

Location : Kolkata, India

WFO (5 days), Hybrid

Immediate Joiner

Must Have

Mastery of Python and strong familiarity with libraries such as NumPy, Pandas, and Scikit-learn.
Extensive hands-on experience with TensorFlow (preferred) or PyTorch (Experience with both is a strong plus).
Strong knowledge of Pattern Recognition and Neural Networks
Solid foundation in Computer Science and Algorithms
Proficiency in Statistics and machine learning concepts
Experience in deploying machine learning models in production environments
Strong understanding of NLP techniques (Tokenization, Embeddings, Transformers, Attention Mechanisms).
Proficiency in SQL and experience handling large datasets.

Good To Have

GenAI Stack : Experience with frameworks like LangChain, LlamaIndex, or Haystack.
Vector Databases : Hands-on experience with vector stores such as Pinecone, Milvus, Weaviate, ChromaDB or FAISS.
Model Tuning : Proven track record of fine-tuning open-source models (e.g., Hugging Face transformers) on custom datasets.
Cloud AI : Experience with AWS SageMaker, Azure AI Studio, or Google Vertex AI.
Big Data : Experience handling large-scale datasets using Apache Spark or Databricks.

Soft Skills

Problem Solver : Ability to break down ambiguous problems into solvable algorithmic components.
Continuous Learner : The AI landscape changes weekly; you must demonstrate a hunger to keep up with the latest papers and techniques.
Communication : Ability to explain complex model behaviors to non-technical stakeholders.

Roles & Responsibilities

Model Development & Engineering :
Design, build, and deploy robust machine learning models using TensorFlow,PyTorch, or Keras for predictive analytics, classification, and computer vision/NLP tasks.
Develop scalable data pipelines to pre-process, clean, and structure structured and unstructured data for model training.
Generative AI & RAG Implementation :
Architect and implement Retrieval-Augmented Generation (RAG) systems to ground LLM responses in proprietary company data.
Orchestrate complex LLM workflows using frameworks like LangChain or LlamaIndex.
Integrate third-party LLM APIs (OpenAI, Anthropic, Gemini) and open-source models (Llama 3, Mistral) into production applications.
Model Tuning & Optimization :
Fine-tune Small Language Models (SLMs) and LLMs for domain-specific tasks using techniques like LoRA, QLoRA, and PEFT to balance performance with computational efficiency.
Optimize model inference latency and throughput for production environments (e.g., using ONNX, TensorRT).
MLOps & Deployment :
Collaborate with DevOps to containerize models (Docker/Kubernetes) and deploy them via TorchServe, TensorFlow Serving, or Triton Inference Server.
Implement experiment tracking and model registry workflows using MLflow or Weights & Biases (W&B).
Technical Leadership :
Mentor junior developers and conduct code reviews.
Translate complex business requirements into technical AI/ML specifications.

(ref:hirist.tech)

Required Skills

Machine Learning Vertex AI Retrieval-Augmented Generation Adobe Illustrator PyTorch Python TensorFlow NLP SQL LlamaIndex Weaviate FAISS Fine-tuning Mistral LoRA PEFT Docker Kubernetes MLflow