Wb Stack developer python, .net kolkata Jobs – 180 Open Jobs Today

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Machine Learning Engineer

CodeClouds -
Kolkata, West Bengal

Apply Now

Job details

Full-time

Benefits

Health insurance
Paid time off

Qualifications

TensorFlow
CI/CD
Statistics
Cloud infrastructure
Azure
Law
Kubernetes
Software deployment
Spark
NumPy
System design
Git
English
Google Cloud Platform
MongoDB
Master's degree
Databases
Pandas
AWS
Analysis skills
Math
Docker
Machine learning
PostgreSQL
Distributed systems
Continuous integration
Deep learning
Redis
REST
SciPy
Natural language processing
GitHub
APIs
Scalability
S3
Design thinking
Training & development
Keras
Flask
SaaS
AI
gRPC
GitLab
Communication skills
Python
NGINX
MySQL

Full job description

Job Details

Available Locations

Kolkata, India

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.
- Clear 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

Apply Now

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Explore jobs in more locations

Roles & Responsibilities

Machine Learning Development

Deep Learning & NLP

Generative AI & LLM Engineering

MLOps & Production Deployment

Data Engineering & Infrastructure

Tools & Technology Stack (Required / Preferred)

Programming & Core Libraries

Deep Learning Frameworks

NLP & LLM Ecosystem

Vector Databases

Databases

MLOps & Experiment Tracking

Deployment & Infrastructure

Cloud Platforms

Data & Distributed Processing

Version Control & Collaboration

Required Technical Knowledge

Soft Skills

Preferred Qualifications

Ideal Candidate Profile

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