Job Details

Available Locations

- Kolkata, India

Category

- Software Developer Jobs

Employment Type

- Full-Time

Seniority

- Senior

Experience Required

- 3+ Years

Skills Required

- Classical Machine Learning, Deep Learning, and Generative AI systems (LLMs & foundation models)

Job Description

We are seeking a highly technical Machine Learning Engineer with strong expertise in classical Machine Learning, Deep Learning, and Generative AI systems (LLMs & foundation models).

The ideal candidate must have hands-on experience designing end-to-end ML pipelines, implementing LLM-powered systems, optimizing models for production, and deploying scalable AI solutions using up-to-date MLOps practices.

This role requires strong mathematical foundations, solid programming skills, system design thinking, and production deployment experience.

Roles & Responsibilities

- 1.

Machine Learning Development

- - Design and implement ML solutions for:
- Classification, regression, clustering
- Anomaly detection
- Recommendation systems
- Time-series forecasting
- Perform feature engineering and advanced data preprocessing.
- Implement
- Cross-validation strategies
- Hyperparameter tuning (Grid, Random, Bayesian)
- Model evaluation pipelines

- Apply ensemble methods such as Boosting, Bagging, and Stacking.
- Optimize models for inference speed, memory usage, and scalability.

- 2.

Deep Learning & NLP

- - Develop and fine-tune deep learning models:
- CNNs, RNNs, LSTMs
- Transformer-based architectures

- Build NLP pipelines:

- Tokenization
- NER
- Semantic similarity
- Text classification

- Fine-tune pretrained transformer models for domain-specific applications.
- Implement attention mechanisms and embedding pipelines.

- 3.

Generative AI & LLM Engineering

- - Integrate Large Language Models into applications.
- Build:
- Retrieval-Augmented Generation (RAG) systems
- Prompt chaining pipelines
- Multi-step reasoning workflows

- Perform:





- Supervised fine-tuning
- LoRA / QLoRA-based parameter-efficient fine-tuning
- Instruction tuning

- Work with:

- Embeddings
- Vector similarity search
- Context management strategies

- Implement:

- Hallucination mitigation techniques
- Output validation & guardrails
- Token usage optimization

- Optimize LLM inference using:

- Quantization
- Model distillation
- Caching strategies

- 4.

MLOps & Production Deployment

- - Deploy models using REST APIs (FastAPI / Flask).
- Containerize ML services using Docker.
- Implement CI/CD pipelines for ML workflows.
- Use model tracking and versioning tools.
- Implement:
- Model monitoring
- Drift detection
- Automated retraining pipelines

- Deploy scalable AI systems on cloud infrastructure.

- 5.

Data Engineering & Infrastructure

- - Build ETL pipelines for large datasets.
- Handle structured & unstructured data.
- Work with distributed systems when required.
- Design scalable data storage architectures.

- Tools & Technology Stack (Required / Preferred)

- Programming & Core Libraries

- - Python (mandatory)
- NumPy
- Pandas
- SciPy
- Scikit-learn

- Deep Learning Frameworks

- - PyTorch
- TensorFlow / Keras

- NLP & LLM Ecosystem

- - Hugging Face Transformers
- Sentence Transformers
- Tokenizers
- LangChain or LLM orchestration frameworks
- OpenAI / Anthropic / other LLM APIs
- RAG pipeline frameworks

- Vector Databases

- - FAISS
- Pinecone
- Weaviate
- Chroma (preferred)

- Databases

- - PostgreSQL
- MySQL
- MongoDB
- Redis

- MLOps & Experiment Tracking

- - MLflow
- Weights & Biases




- DVC (preferred)

- Deployment & Infrastructure

- - FastAPI / Flask
- Docker
- Kubernetes (preferred)
- Nginx (preferred)
- REST / gRPC services

- Cloud Platforms

- - AWS (SageMaker, EC2, S3)
- Azure ML
- Google Cloud AI Platform

- Data & Distributed Processing

- - Apache Spark (preferred)
- Airflow (preferred)

- Version Control & Collaboration

- - Git (branching strategies, pull requests, code reviews)
- GitHub / GitLab / Bitbucket

- Required Technical Knowledge

- - Strong understanding of:
- Linear algebra
- Probability & statistics
- Optimization algorithms

- Deep knowledge of:

- Transformer architecture
- Attention mechanisms
- Embedding models

- Experience with:

- Model quantization
- Inference optimization
- Latency & cost optimization in LLM systems

- Understanding of Responsible AI:

- Bias mitigation
- Fairness
- Model explainability

- Soft Skills

- - Strong analytical thinking.
- Explicit documentation practices.
- Ability to translate business problems into ML solutions.
- Research-oriented and experimentation mindset.
- Strong English communication skills.

- Preferred Qualifications

- - Experience with multimodal AI (text + image/audio).
- Experience with diffusion models.
- Distributed training (multi-GPU).
- Experience building production-scale AI SaaS products.
- Contributions to open-source or research publications.

- Ideal Candidate Profile

- - Strong ML theoretical foundation.
- Production-first mindset.
- Deep hands-on experience with GenAI.
- System design and scalability oriented.
- Passionate about cutting-edge AI technologies.

What we offer you

- Flexible Working

- Competitive Compensation

- Insurance Benefits

- Training & Mentoring

- Frequent Celebrations

- Home Office Allowance

- Paid Leave Benefits

- Retirement Benefits

- Partial Course Funding

- Team Building Activities

📌 Machine Learning Engineer (Kolkata)
🏢 CodeClouds
📍 Kolkata