Description :

Role Overview

We are seeking an experienced AI / Machine Learning Engineer who will design, develop, deploy, and optimize AI-driven solutions for real-world business applications.

The ideal candidate should possess hands-on expertise in Machine Learning, NLP, Generative AI, model deployment, and API development, and be comfortable working with large-scale structured and unstructured datasets.

The candidate will collaborate closely with product managers, data engineers, and business stakeholders to convert complex business challenges into robust, scalable AI systems.

Key Responsibilities :

- Design, train, evaluate, and deploy machine learning and deep learning models for real-world applications

- Implement and optimize NLP-based solutions, including Zero-Shot and Few-Shot classification

- Apply modern text representation techniques such as TF-IDF, Word2Vec, BERT, Sentence Transformers

- Develop, fine-tune, and deploy Generative AI models using OpenAI, LLaMA, or similar foundation models

Experiment with ML algorithms including :

1. Linear/Logistic Regression

2. Decision Trees & Random Forests

3. SVM, KNN

4. Ensemble & Bayesian models

- Build, maintain, and monitor RESTful APIs and microservices using Flask/FastAPI for model serving

- Optimize models for latency, scalability, and cost efficiency in production environments

- Build reusable MLOps workflows for training, validation, and deployment pipelines

- Develop end-to-end data pipelines and integrate structured and unstructured data from MySQL and MongoDB

- Perform feature engineering, model explainability, and error analysis

- Collaborate with cross-functional teams to convert requirements into AI product features

- Maintain detailed documentation including datasets, experiments, model versions, and deployments

- Use Git/Bitbucket for version control, branching, and code reviews

- Track and manage deliverables through JIRA/Asana or similar project management tools

- Stay current with latest research in NLP, LLMs, and Generative AI and evaluate feasibility for business use cases

Key Result Areas (KRAs) :

- Model performance (accuracy, F1 score, precision/recall, ROUGE/BLEU for NLP etc.)

- Successful deployment of AI models into production

- Reduction in manual effort through automation & AI solutions

- API response time, uptime, and scalability metrics

- Business KPIs impacted by AI solution adoption

- Quality of documentation and reproducibility of results

- Timely delivery of AI features in alignment with product roadmap

Required Qualifications & Experience :

Bachelors or Masters degree in :

- Computer Science

- Engineering

- Mathematics

- Statistics

- 3+ years of hands-on experience in ML/NLP/AI model development and deployment

Technical Skill Requirements :

- Machine Learning & Deep Learning

- Supervised and unsupervised algorithms

- Feature engineering & model tuning

- Generative AI / LLMs experience preferred

- NLP & Generative AI

- Zero-Shot / Few-Shot learning

- Transformers and Embeddings

- LLM fine-tuning or prompting

- Programming & Frameworks

- Python is mandatory

Experience with libraries such as :

- scikit-learn

- TensorFlow / PyTorch

- Transformers (HuggingFace)

- Model Serving & Engineering

- Flask / FastAPI

- RESTful API design

- Microservices

- Data & Storage

- MongoDB

- MySQL

- Data wrangling using Pandas/NumPy

- Dev & Collaboration Tools

- Git / Bitbucket

- JIRA / Asana

Preferred Certifications (Nice to Have) :

- Machine Learning (Coursera/Stanford/Google)

- Deep Learning Specialization

- NLP certification

- Generative AI or LLM certification

Soft Skills :

- Strong analytical and problem-solving ability

- Ability to work independently and in teams

- Clear verbal and written communication

- Curiosity for research and experimentation

- Product-thinking mindset