AI/ML & Generative AI Developer

1-2 Years Experience | Tecorb Technologies

Noida | Full-Time | IT Services

Job Title

Experience

Location

Job Type

Industry

AI/ML & Generative AI Developer

1-2 Years

Noida

Full-Time

IT Services

About the Role

At Tecorb Technologies, we are building the future of intelligent systems by leveraging cutting-edge

AI/ML and Generative AI technologies.

We are looking for a driven and technically grounded developer with 1-2 years of hands-on experience to join our AI/ML & Generative AI team. This role is suited for professionals who have moved beyond entry-level exposure and are ready to take ownership of AI/ML pipelines, contribute to real-world deployments, and grow into a senior AI engineer role. You will work on machine learning, deep learning, large language models (LLMs), and generative AI systems including GPT models,

Transformers, diffusion models, and more.

If you connect dots, understand systems holistically, and are passionate about building intelligent applications we want you.

Core AI/ML Knowledge Required

Machine Learning Fundamentals

Supervised, unsupervised, and semi-supervised learning paradigms

Classical ML algorithms: Linear/Logistic Regression, Decision Trees, Random Forests, SVMs,

Gradient Boosting (XGBoost, LightGBM)

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Model evaluation: cross-validation, bias-variance tradeoff, precision/recall, ROC-AUC

Feature engineering, data preprocessing pipelines, and handling imbalanced datasets

Hands-on experience with scikit-learn, NumPy, Pandas

Deep Learning & Neural Networks

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Solid understanding of feedforward networks, backpropagation, and optimization (Adam, SGD,

learning rate schedulers)

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Convolutional Neural Networks (CNNs) for image/vision tasks

Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequential data

Attention mechanisms and how they led to the Transformer architecture

Experience with PyTorch and/or TensorFlow/Keras for model training and fine-tuning

Transformers & Large Language Models

Transformers are the backbone of modern AI. Candidates must have strong knowledge of:

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Transformer Architecture Self-attention, multi-head attention, positional encoding,

encoder-decoder structure

BERT (Bidirectional Encoder Representations from Transformers) masked language modeling,



fine-tuning for classification, NER, QA

GPT Models (GPT-2, GPT-3, GPT-3.5, GPT-4) causal language modeling, prompt engineering, in-context learning, and API integration

T5, FLAN-T5 encoder-decoder models for text-to-text tasks like summarization, translation,

and QA

LLaMA, Mistral, Falcon open-source LLM architectures, weight loading, and inference optimization

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Hugging Face Transformers library model loading, tokenization, pipelines, and Trainer API

Tokenization strategies BPE, WordPiece, SentencePiece

Positional encodings absolute, relative (ALiBi, RoPE)

Generative AI Systems

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Generative Adversarial Networks (GANs) basics of generator-discriminator training

Variational Autoencoders (VAEs) latent space, reconstruction loss, KL divergence

Diffusion Models forward/reverse diffusion, DDPM, Stable Diffusion, ControlNet

Retrieval-Augmented Generation (RAG) vector retrieval, chunking strategies, reranking

Prompt engineering techniques zero-shot, few-shot, chain-of-thought, ReAct

Fine-tuning strategies full fine-tuning, LoRA, QLoRA, instruction tuning, RLHF basics

NLP Fundamentals

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Text preprocessing tokenization, lemmatization, stopword removal, vectorization (TF-IDF,

embeddings)

Core NLP tasks sentiment analysis, Named Entity Recognition (NER), text classification,

summarization, machine translation

Word embeddings Word2Vec, GloVe, FastText, and contextual embeddings from

Transformers

Semantic similarity, question answering, and dialogue systems

Key Responsibilities

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Design, build, fine-tune, and deploy ML models and generative AI pipelines with minimal supervision

Integrate pre-trained models (GPT-4, LLaMA, Mistral, Stable Diffusion) into production applications via APIs or custom backends

Implement end-to-end NLP systems chatbots, content generators, summarizers, and RAG pipelines

Perform data preprocessing, feature engineering,



and dataset curation for training and evaluation

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Conduct model evaluation, benchmarking, error analysis, and iterative performance tuning

Explore and implement reinforcement learning techniques (RLHF, reward modeling) to improve model interactions

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Build modular, scalable AI workflows using FastAPI, Docker, LangChain, and vector databases

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Stay current with AI research (arXiv, Hugging Face, OpenAI blogs) and prototype new ideas rapidly

Write clear documentation for experiments, code, models, pipelines, and datasets

Required Skills & Qualifications

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Bachelors, Masters, or PhD in Computer Science, Data Science, AI/ML, or a related field

1-2 years of hands-on professional or project experience with ML/AI systems

Strong Python proficiency with ML libraries: scikit-learn, TensorFlow, PyTorch

Solid understanding of Transformer architecture and attention mechanisms

Practical experience working with GPT models via API (OpenAI, Azure OpenAI, Groq) or open-source equivalents

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Working knowledge of Hugging Face ecosystem (Transformers, Datasets, PEFT)

Familiarity with LLM fine-tuning techniques: LoRA, QLoRA, or full fine-tuning

Understanding of vector databases and embedding workflows (FAISS, ChromaDB, Pinecone)

Knowledge of APIs, JSON, and RESTful application design

Proficiency with Git, Jupyter Notebooks, and Linux command-line tools

Nice to Have (Not Mandatory)

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Familiarity with MLOps tools: Weights & Biases, MLflow, DVC, or TensorBoard

Basic knowledge of reinforcement learning

What Youll Get

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Hands-on experience on real AI/ML projects and production-ready systems from day one

Mentorship and career development from senior AI/ML engineers and researchers

Access to state-of-the-art tools, compute infrastructure, and paid API credits

Opportunity to contribute to high-impact solutions in AI automation, GenAI workflows, and enterprise applications

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A learning-driven, inclusive, and collaborative work environment

Rapid growth track with exposure to client-facing work, research, and system design

We are looking for an Engineer who understands systems and connects the dots not just a developer.

Tecorb Technologies | Noida | tecorb.com

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