Urgent Opening For AI/ML Engineer - Immediate Joiner

Tuteck; Kolkata District, New Delhi; Vor Ort; INR 1.800.000 - 3.600.000; Vollzeit

Zusammenfassung

Tuteck is seeking a Senior AI/ML Engineer to lead the development of next-generation intelligence platforms. The role spans data pipelines, model training, deployment, and monitoring with a focus on Generative AI, RAG architectures, and LLM/SLM integration.

The candidate will implement end-to-end ML workflows, optimize inference latency, and guide junior developers, translating business needs into AI/ML specifications. Strong Python expertise and production experience are required.

Qualifikationen

• Education: Bachelors or Masters in Computer Science, Data Science, Mathematics, or related field.
• 2–7 years of professional Software Engineering with focus on AI/ML.
• Proficient in Python with NumPy, Pandas, Scikit-learn.
• Hands-on experience with TensorFlow or PyTorch (TensorFlow preferred).
• Strong knowledge of neural networks, CS fundamentals and algorithms.
• Experience deploying ML models in production and NLP techniques.

Aufgaben

• Design, build and deploy ML models for predictive analytics and NLP tasks.
• Develop scalable data pipelines for model training on structured and unstructured data.
• Architect RAG systems and integrate LLM APIs and open-source models.
• Fine-tune LLMs/SLMs using LoRA, QLoRA, PEFT for domain tasks.
• Collaborate with DevOps to containerize models and deploy via TorchServe or Triton.

Kenntnisse

Python

NumPy

Pandas

Scikit-learn

TensorFlow

PyTorch

Keras

NLP

SQL

MLOps

LoRA

Ausbildung

Bachelor's or Master's in CS/DS

Tools

Docker/Kubernetes

TorchServe/TensorFlow Serving

MLflow

Weights & Biases

LangChain

LlamaIndex

Haystack

Pinecone

Jobbeschreibung

Role Description

• We are looking for a highly skilled Senior AI/ML Engineer to lead the development of our next-generation intelligence platforms. In this role, you will bridge the gap between traditional predictive modeling and cutting-edge Generative AI. You will be responsible for the full lifecycle of ML developmentfrom data pipeline construction and model training to deployment and monitoring.
• Ideal candidates will have deep hands-on experience with major deep learning frameworks and a strong passion for integrating Large Language Models (LLMs) and Small Language Models (SLMs) using RAG architectures.

Key Responsibilities

1. 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.

2. 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.

3. 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).

4. 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>).

5. Technical Leadership

• Mentor junior developers and conduct code reviews.
• Translate complex business requirements into technical AI/ML specifications.

Required Qualifications

• Education: Bachelors or Masters degree in Computer Science, Data Science, Mathematics, or a related field.
• Experience: 2 to 7 years of professional experience in Software Engineering with a specific focus on AI/ML.
• 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.

Preferred Experiences

• 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 & Competencies

• 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.