AI/ML & Generative AI Developer
Tecorb Technologies —Noida, Uttar Pradesh
- Azure
- Doctoral degree
- Computer Science
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AI/ML & Generative AI Developer

Tecorb Technologies -
Noida, Uttar Pradesh

Apply Now

Job details

10 days ago

Qualifications

TensorFlow
Azure
Law
Doctoral degree
Computer Science
PyTorch
NumPy
System design
Git
Master's degree
Databases
Pandas
Docker
Sentiment analytics
Bachelor's degree
Machine learning
Deep learning
REST
Natural language processing
APIs
Regression analysis
Linux
JSON
Data science
Keras
AI
Python

Full job description

We Promise You An Inclusive Work Environment

AI/ML & Generative AI Developer

Job Description

About the Role

Experience: 1-2 years

University/Education: Graduated from a reputable college/university or has worked on projects they are proud of and can confidently showcase.

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)
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

Solid understanding of feedforward networks, backpropagation, and optimization (Adam, SGD, learning rate schedulers)
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:

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
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

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

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

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
Conduct model evaluation, benchmarking, error analysis, and iterative performance tuning
Explore and implement reinforcement learning techniques (RLHF, reward modeling) to improve model interactions
Build modular, scalable AI workflows using FastAPI, Docker, LangChain, and vector databases
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

Bachelor’s, Master’s, 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
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)

Familiarity with MLOps tools: Weights & Biases, MLflow, DVC, or TensorBoard
Basic knowledge of reinforcement learning

What You’ll Get

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
A learning-driven, inclusive, and collaborative work environment
Fast 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

Apply Now

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AI/ML & Generative AI Developer

Job Description

About the Role

Core AI/ML Knowledge Required

Machine Learning Fundamentals

Deep Learning & Neural Networks

Transformers & Large Language Models

Generative AI Systems

NLP Fundamentals

Key Responsibilities

Required Skills & Qualifications

Nice to Have (Not Mandatory)

What You’ll Get