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
