AI/ML Engineer

Location: Onsite

Experience: 2+ Years

Employment Type: Full-time 

 

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. 

 

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. 

 

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). 

 

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. 

 

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

 

Strong analytical and problem-solving mindset. 

Excellent communication and teamwork abilities. 

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