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AI/ML Engineer

SentientGeeks -
Unknown, West Bengal

Apply Now

Job details

Full-time

Qualifications

Power BI
Image processing
Law
Computer vision
Software deployment
NoSQL
Git
MongoDB
Tableau
Master's degree
Databases
Microservices
SQL
MCA
Analysis skills
Database management
Docker
Bachelor's degree
Machine learning
Deep learning
RabbitMQ
Back-end development
REST
Natural language processing
Django
GitHub
APIs
Scalability
Kafka
Flask
AI
GitLab
Communication skills
Python
MySQL

Full job description

SentientGeeks is Looking for an AI/ML Backend Engineer (2–3 Years Experience)

Location: Onsite

Experience: 2+ Years

Employment Type: Full-time

About the Role

SentientGeeks is seeking a passionate and skilled AI/ML Backend Engineer to join our growing Artificial Intelligence team. The ideal candidate should have a strong foundation in Deep Learning, NLP, Python, Machine Learning, and database management, with hands-on experience in building and integrating backend systems for AI-driven applications.

Must-Have (Mandatory Skills)

Deep Learning & NLP: Practical experience in building, fine-tuning, and deploying deep learning models for NLP tasks such as embeddings, classification, and retrieval (RAG) pipelines.

Machine Learning: Solid understanding of ML workflows — data preprocessing, model training, evaluation, and deployment.

Python Programming: Strong proficiency in Python for backend and ML model integration.

Vector Databases: Hands-on experience with one or more — FAISS, Pinecone, Weaviate, or PyMilvus.

Databases:

SQL: Strong in MySQL

NoSQL: Strong in MongoDB

Backend Development: Expertise in developing RESTful APIs / microservices using FastAPI, Flask, or Django.

Data Handling: Ability to manage structured and unstructured data in ML pipelines.

Version Control: Proficiency with Git / GitHub / GitLab for collaborative development.

Model Deployment: Experience deploying AI/ML models into production environments and optimizing inference performance.

Good-to-Have (Preferred / Bonus Skills)

MLOps: Familiarity with tools like MLflow, Kubeflow, Airflow, or Seldon.

Computer Vision: Understanding of image-based model development and deployment.

RPA Integration: Knowledge of Blue Prism or UiPath integration with AI components.

Generative AI & LLM Tools: Experience with LangChain, LangGraph, LangSmith, Langflow, and similar frameworks.

Agentic AI Frameworks: Exposure to AutoGen (Microsoft) or CrewAI.

Workflow Automation: Familiarity with n8n, Airflow, or other orchestration tools.

Vector DB Optimization: Experience in tuning FAISS, Weaviate, or PyMilvus for scalability.

Containerization: Working knowledge of Docker for packaging and deployment.

Event Streaming: Basic understanding of Kafka or RabbitMQ.

GenAI Integrations: Practical knowledge of OpenAI, Hugging Face, or custom LLMs.

Business Intelligence (BI): Exposure to BI dashboards or data visualization tools (e.g., Power BI, Tableau).

Key Responsibilities

Design and maintain scalable backend systems to support AI/ML workflows.

Integrate and serve deep learning/NLP models in production environments.

Manage and query vector databases for semantic and similarity-based retrieval.

Build secure and optimized APIs for AI-driven applications.

Collaborate with data scientists to transform prototypes into deployable solutions.

Implement automation and monitoring for model lifecycle management.

Contribute to AI architecture discussions involving GenAI and agentic workflows.

Educational Qualification

B.Tech / M.Tech / MCA / M.Sc in Computer Science, IT, or equivalent field.

Soft Skills

Strong analytical and problem-solving mindset.

Excellent communication and teamwork abilities.

Eagerness to learn and explore new AI, MLOps, and GenAI frameworks

Apply Now

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