Senior AI/ML Engineer

Company Description

TuTeck Technologies is a global technology company that empowers enterprises to transform through data-driven insights and digital innovation. Leveraging a leadership team with over 500 years of combined experience, TuTeck delivers solutions in AI & ML Engineering, Data Management, Business Intelligence, and more. Operating from locations in London (UK), Kolkata (India), and Delaware (USA), TuTeck is home to a talented team of over 200 consultants. Recognized with the NASSCOM SME Inspire Award and certified for SOC Type2, ISO 9001:2015, and ISO 27001:2022, TuTeck maintains the highest standards of quality and innovation. Their solutions are designed to streamline operations, enhance customer experience, and drive business growth.

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 development—from 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:
  Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related field.
• Experience:
  5–8 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 (T
  okenization, 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.