Software Engineer I - AI and ML
Noida, Uttar Pradesh, India
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDesign, develop, and deploy ML models and Generative AI solutions
Own end-to-end ML pipelines, from data ingestion to production monitoring
Translate business requirements into AI-driven product features
Build Python RESTful APIs that expose ML models and AI services
Integrate RAG pipelines, LLM fine-tuning, and vector search into live products
From the employer’s posting
Design, develop, and deploy machine learning models and Generative AI solutions
Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
Collaborate with cross-functional teams to translate business requirements into AI-driven features
Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products
What you’ll bring
All qualificationsCore experience
- You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.
- Python Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
Qualification wording
You are a passionate Python and AI/ML Engineer minimum 5 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.
Python Strong hands-on proficiency for building, scripting, and deploying AI/ML systems. NumPy · Pandas · FastAPI · Scikit-learn Machine Learning Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection. PyTorch · TensorFlow · XGBoost · DBSCAN
Tools in this posting
- JavaScript
- Python
- SQL
- TypeScript
- AWS
- Azure
- Docker
- Elasticsearch
- GCP
- Kubernetes
- MLflow
- MongoDB
- NoSQL
- PostgreSQL
- FastAPI
- NumPy
- PyTorch
- React
- scikit-learn
- TensorFlow
- XGBoost
- Node.js
Source — Tool mentions in context
Cloud Platforms Deploys and scales AI workloads on AWS, Azure, or GCP. AWS · Azure TypeScript / JavaScript Frontend or full-stack exposure for building ML-powered product interfaces. TypeScript · React · Node.js MLOps Manages the ML lifecycle — tracking, versioning, and pipeline automation. MLflow · Kubeflow · CI/CD
WHO YOU ARE You are a passionate Python and AI/ML Engineer minimum 5 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do. WHAT YOU'LL DO
- Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable. - Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments. - Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
WHAT YOU'LL NEED Python Strong hands-on proficiency for building, scripting, and deploying AI/ML systems. NumPy · Pandas · FastAPI · Scikit-learn Machine Learning Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection. PyTorch · TensorFlow · XGBoost · DBSCAN Generative AI (2+ yrs) Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features. LLMs · RAG · Prompt Eng. · Fine-tuning
- Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments. - Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems. - Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.
API Development Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services. REST · FastAPI · OAuth2 · Swagger Databases Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads. PostgreSQL · MongoDB · Vector DBs GOOD TO HAVE
GOOD TO HAVE Cloud Platforms Deploys and scales AI workloads on AWS, Azure, or GCP. AWS · Azure TypeScript / JavaScript Frontend or full-stack exposure for building ML-powered product interfaces. TypeScript · React · Node.js
MLOps Manages the ML lifecycle — tracking, versioning, and pipeline automation. MLflow · Kubeflow · CI/CD Containerization & Orchestration Packages and scales AI services using containers and cluster management. Docker · Kubernetes
Generative AI (2+ yrs) Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features. LLMs · RAG · Prompt Eng. · Fine-tuning NLP & Search / Ranking Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models. spaCy · BERT · FAISS · Elasticsearch API Development Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services. REST · FastAPI · OAuth2 · Swagger
TypeScript / JavaScript Frontend or full-stack exposure for building ML-powered product interfaces. TypeScript · React · Node.js MLOps Manages the ML lifecycle — tracking, versioning, and pipeline automation. MLflow · Kubeflow · CI/CD Containerization & Orchestration Packages and scales AI services using containers and cluster management. Docker · Kubernetes
NLP & Search / Ranking Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models. spaCy · BERT · FAISS · Elasticsearch API Development Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services. REST · FastAPI · OAuth2 · Swagger Databases Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads. PostgreSQL · MongoDB · Vector DBs
About Zinnia - Employee Referral
Zinnia is the leading technology platform for accelerating life and annuities growth.
In the employer’s words · Read in context
Posting record
Report an errorOpen 4 monthsWe first saw this posting 149 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026 · 149 days ago
- Recorded sightings
- 57
- Last seen by us
- Oct 21, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
WHO WE ARE:
Zinnia is the leading technology platform for accelerating life and annuities growth. With innovative enterprise solutions and data insights, Zinnia simplifies the experience of buying, selling, and administering insurance products. All of which enables more people to protect their financial futures. Our success is driven by a commitment to three core values: be bold, team up, deliver value – and that we do. Zinnia has over $180 billion in assets under administration, serves 100+ carrier clients, 2500 distributors and partners, and over 2 million policyholders.
WHO YOU ARE
You are a passionate Python and AI/ML Engineer minimum 5 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.
WHAT YOU'LL DO
- Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
- Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
- Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
- Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
- Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
- Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.
WHAT YOU'LL NEED
Python
Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
NumPy · Pandas · FastAPI · Scikit-learn
Machine Learning
Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
PyTorch · TensorFlow · XGBoost · DBSCAN
Generative AI (2+ yrs)
Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
LLMs · RAG · Prompt Eng. · Fine-tuning
NLP & Search / Ranking
Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
spaCy · BERT · FAISS · Elasticsearch
API Development
Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
REST · FastAPI · OAuth2 · Swagger
Databases
Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
PostgreSQL · MongoDB · Vector DBs
GOOD TO HAVE
Cloud Platforms
Deploys and scales AI workloads on AWS, Azure, or GCP.
AWS · Azure
TypeScript / JavaScript
Frontend or full-stack exposure for building ML-powered product interfaces.
TypeScript · React · Node.js
MLOps
Manages the ML lifecycle — tracking, versioning, and pipeline automation.
MLflow · Kubeflow · CI/CD
Containerization & Orchestration
Packages and scales AI services using containers and cluster management.
Docker · Kubernetes
Source details
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- Pay
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- Location & working pattern
Noida, Uttar Pradesh, India
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