AI / ML Engineer — Automation, Data Pipelines & Agentic Systems (Mid-Level)
Role Overview
We are seeking an AI/ML Engineer with 3–5 years of experience building production machine learning systems, automation pipelines, agentic systems and AI-powered workflow orchestration.
The role focuses on designing intelligent systems that ingest data from multiple enterprise and external sources, transform and normalize that data, and use machine learning and large language models to generate insights, trigger automated workflows, and support operational decision-making.
This engineer will work across machine learning engineering, backend development, data pipelines, and AI automation to build systems that power analytics, pattern detection, forecasting, and automated business processes.
Key Responsibilities
Machine Learning & AI Systems
- Design, train, and deploy machine learning models for predictive analytics, pattern detection, classification, and recommendation systems.
- Build pipelines that enable data-driven inference using machine learning and large language models.
- Develop AI-driven systems that generate insights and trigger automated actions based on model outputs.
- Optimize model performance and maintain training and inference pipelines.
Agentic Workflow Development
- Design and implement agent-driven workflows capable of executing multi-step automation processes.
- Build systems where AI agents monitor events, interpret context, and initiate automated actions across enterprise platforms.
- Implement orchestration logic connecting APIs, databases, automation tools, and AI models.
Typical examples include:
- CRM-triggered workflows (e.g., lead created → automated qualification → outbound campaign initiation).
- AI-assisted lead nurturing pipelines.
- Automated campaign orchestration triggered by operational events.
- AI-based decision logic driving operational workflows.
Automation Pipeline Development
- Build event-driven automation systems integrating enterprise platforms and external APIs.
- Develop automated pipelines that fetch, process, and analyze operational and marketing data.
- Implement automation workflows that trigger downstream processes based on analytics or AI-driven insights.
Example pipelines include:
- Fetching data from Google Ads, CRM systems, marketing platforms, and internal tools.
- Triggering automated workflows based on campaign performance or lead activity.
- Real-time operational automation based on incoming data events.
Data Pipeline & Processing
- Develop pipelines that collect and process data from multiple sources including APIs, web services, and data feeds.
- Implement data transformation processes including:
- Data cleaning
- Data sanitization
- Data normalization
- Data standardization
- Prepare structured datasets for machine learning and LLM-based inference systems.
- Maintain scalable pipelines for batch and real-time data processing.
Data Inference & Analytics Automation
- Build AI-powered analytics pipelines that convert raw operational data into insights and automated actions.
- Implement systems capable of:
- Pattern detection
- Trend analysis
- Forecasting and projections
- Operational anomaly detection
- Develop automation pipelines that feed processed data into LLMs or ML models for:
- Insight generation
- Data interpretation
- Operational decision support.
Data Acquisition & Scraping
- Develop systems to extract and ingest data from external sources where APIs may not be available.
- Implement web data extraction pipelines with appropriate data cleaning and structuring processes.
- Ensure reliability, compliance, and efficiency of automated data ingestion systems.
Application & Platform Integration
- Build backend services that expose AI capabilities and automation workflows through APIs.
- Contribute to application services developed using Node.js and TypeScript.
- Integrate AI capabilities into applications built using React and Next.js.
- Support product teams in embedding AI features within enterprise platforms.
Model Deployment & MLOps
- Deploy ML models into production environments.
- Implement monitoring, logging, and retraining pipelines.
- Maintain reproducible ML experimentation workflows.
- Collaborate with DevOps teams to ensure scalable infrastructure.
Required Technical Skills
Programming
- Strong proficiency in Python for machine learning, data processing, and automation pipelines.
- Experience with JavaScript / TypeScript for backend services and API integration.
Machine Learning
Hands-on experience with:
- PyTorch or TensorFlow
- Scikit-learn
- Hugging Face or transformer-based models
- Model training, evaluation, and deployment
Data Engineering & Processing
- Pandas, NumPy
- SQL
- Data pipeline development
- Experience building data ingestion and transformation workflows
Automation & AI Orchestration
Experience building or working with:
- Agent-based AI systems
- Event-driven architectures
- Workflow orchestration frameworks
- LLM orchestration tools such as LangChain or LlamaIndex
Application Stack
Working familiarity with:
- React
- Next.js
- Node.js
- TypeScript
Infrastructure
- Docker and containerized services
- Cloud platforms such as AWS, GCP, or Azure
- Scalable deployment architectures
Required Experience
- Integrating systems with Salesforce, Google Ads, marketing platforms, or analytics systems.
- Building AI-driven analytics pipelines.
- Experience with vector databases and embedding pipelines.
- Development of Retrieval-Augmented Generation (RAG) systems.
- Experience with data scraping and automated data extraction pipelines.
Technology Environment
Frontend
React, Next.js, TypeScript
Backend
Node.js, REST APIs, Webhooks
AI / ML
Python, PyTorch / TensorFlow, Hugging Face
Automation & Orchestration
LangChain, LLM orchestration frameworks, API integrations
Data
SQL databases, data pipelines, analytics pipelines
Infrastructure
Docker, Cloud platforms (AWS / GCP / Azure)
Success Metrics
- Reliable AI-driven automation pipelines across enterprise platforms.
- Accurate machine learning models generating actionable insights.
- Scalable data pipelines supporting analytics and inference workflows.
- Effective integration of AI capabilities into operational systems.
Experience Profile
Ideal candidates typically demonstrate:
- 3–5 years of hands-on experience in ML engineering, AI systems, or data-driven automation.
- Experience building production automation pipelines and AI-powered analytics systems.
- Practical exposure to data ingestion, AI inference pipelines, and enterprise workflow automation.
Job Types: Full-time, Contractual / Temporary
Contract length: 6 months
Pay: From ₹50,000.00 per month
Benefits:
- Flexible schedule
- Paid sick time
- Work from home
Experience:
- React: 1 year (Preferred)
- TypeScript: 1 year (Preferred)
- NextJS : 1 year (Preferred)
Work Location: Remote
