Karnataka Network systems operator Jobs – 130 Open Jobs Today

ML Engineer (Forward Deployed)
Applied Computing —Bengaluru, Karnataka
- Computer Science
- Master's degree
- Databases
AI Platform Engineer
eBay Inc. —Bengaluru, Karnataka
- Full-time
- Computer Science
- Master's degree
- Administrative experience
Software Engineering Technical Leader
Cisco Systems —Bengaluru, Karnataka
- Full-time
- Computer Science
- Bachelor's degree
- APIs
Staff Site Reliability Operations Engineer
Calix —Bengaluru, Karnataka
- Email
- Remote
- Machine learning
- Linux
- AI
13d
Senior Infrastructure Engineer
Ema —Bengaluru, Karnataka
- Full-time
- Azure
- Computer Science
- Databases
Onshore Piping Engineer
Chevron —Bengaluru, Karnataka
- Full-time
- Master's degree
- Bachelor's degree
- Construction
- Health insurance
Nice- Consultant, Technical Transformation- Sales & Service- Customer
Deloitte —Bengaluru, Karnataka
- Salesforce
- Java
- Master's degree
Full Stack Engineer, Forward Deployed
Applied Computing —Bengaluru, Karnataka
- Master's degree
- Databases
- JavaScript
Senior Sales Director
First Connect Human Capital Services LLP —Bengaluru, Karnataka
- Full-time
- Sales
- Marketing
- Engineering
Quick apply
7d
Support Engineer II, Alexa 3PEO
ADCI - BLR 14 SEZ —Bengaluru, Karnataka
- Full-time
- Software troubleshooting
- Engineering
- Master's degree
R&D Senior Developer GMS - IDC
Hitachi —Bengaluru, Karnataka
- Full-time
- Doctoral degree
- Computer Science
- Windows
Director of Platform, India
xponentiate —Bengaluru, Karnataka
- Full-time
- Hybrid work
- English
- Master's degree
- AI
Quick apply
AI Infra Architect- Lead Cloud Integrated Infrastructure Engineer II
Deloitte —Bengaluru, Karnataka
- Computer Science
- Master's degree
- Bachelor's degree
10d
Regional Solutions Engineer for Asia Pacific South
Viavi Solutions —Bengaluru, Karnataka
- Full-time
- B.Sc
- English
- Bachelor's degree
Software Development Manager, UTR Tech
ADCI - Karnataka —Bengaluru, Karnataka
- Full-time
- Management
- Machine learning
- Team management
Shopfloor Apps Solution Architect
PHINIA —Bengaluru, Karnataka
- Full-time
- Azure
- Computer Science
- Master's degree
- Health insurance
Offensive Security / Compromise Assessment - Senior Manager
Grant Thornton INDUS —Bengaluru, Karnataka
- Azure
- Analysis skills
- Team management
Sr DevOps Engineer
SkanAI —Bengaluru, Karnataka
- Azure
- DevOps
- Agile
Quick apply
Developer
IP Infusion US Inc. —Bengaluru, Karnataka
- Full-time
- Master's degree
- Analysis skills
- Bachelor's degree
Sr. DevOps Manager
Amol Technologies —Bengaluru, Karnataka
- Full-time
- Azure
- DevOps
- AWS

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ML Engineer (Forward Deployed)

Applied Computing -
Bengaluru, Karnataka

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Job details

Qualifications

CI/CD
Law
Computer Science
Kubernetes
PyTorch
Oil & gas
Master's degree
Databases
Microservices
SQL
AWS
Docker
Machine learning
Distributed systems
Continuous integration
Deep learning
RabbitMQ
REST
APIs
Distributed control systems
Kafka
Data science
AI
Graph databases
Python
Debugging

Full job description

Applied Computing was founded in 2023 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable
abundance for a growing planet.

The hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data. We built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational
data and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls.

We’ve raised over $32 million, including one of the largest seed rounds for an
AI company in the UK. We’re just getting started

As a Forward Deployed ML Engineer, your job is to make Orbital’s AI systems work in customer reality. You will deploy, configure, tune, and operationalise our deep learning models inside live industrial environments; spanning cloud, on-premise, hybrid, and air-gapped infrastructure.

This is not a pure research role.

You are not training experimental models in isolation. You are adapting production AI systems to customer data, configuring agents and RAG pipelines, tuning anomaly detection, and ensuring models deliver value in production workflows.

If Research builds the models, you make them work on-site.

Operating Context
Forward Deployed ML Engineers operate in pods of three alongside:

Full Stack Engineers
Data Engineers

Each pod delivers 2–3 customer deployments per quarter, owning AI configuration, model tuning, agent orchestration, and inference reliability in production.

MSc in Computer Science, Machine Learning, Data Science, or related field, or equivalent practical experience.
Strong proficiency in Python and deep learning frameworks (PyTorch preferred).
Solid software engineering background; designing and debugging distributed systems.
Experience building and running Dockerised microservices, ideally with Kubernetes/EKS.
LLM API integrations (OpenAI, Claude, Gemini), FastAPI for ML services and REST inference APIs
Familiarity with message brokers (Kafka, RabbitMQ, or similar).
Comfort working in hybrid cloud/on-prem deployments (AWS, Databricks, or industrial environments).
Exposure to time-series or industrial data (historians, IoT, SCADA/DCS logs) is a plus.
Domain experience working as a data scientist in oil and gas or energy is a plus.
Ability to work in forward-deployed settings, collaborating directly with customers.
Comfortable in customer-facing technical roles.
Able to operate in forward-deployed environments.
Strong troubleshooting capability in production AI systems

What Success Looks Like

AI systems are deployed and running in customer environments.
Models are tuned to customer data and delivering operational value.
Anomalies and predictions are trusted by engineers.
Multi-agent copilots function reliably in production workflows.
RAG systems retrieve accurate, domain-relevant insights.
Inference pipelines run with high uptime and low latency.

1) AI System Deployment & Configuration

Deploy Orbital’s AI/ML services into customer environments.
Configure inference pipelines across cloud, on-prem, and hybrid infrastructure.
Package and deploy ML services via Docker/Kubernetes.
Ensure inference services are reliable, scalable, and production-ready.

2) Time Series & Predictive Model Tuning

Deploy and tune time-series forecasting and anomaly detection models.
Adapt models to customer-specific industrial processes.
Configure thresholds, alerting logic, and detection sensitivity.
Validate model outputs against engineering expectations.

Typical model classes include:

Gradient boosting models (LightGBM)
Transformer models
Statistical anomaly detection methods
Multivariate monitoring systems

3) Multi-Agent & LLM System Configuration

Deploy and configure multi-agent AI systems for customer workflows.
Set up LLM provider integrations (OpenAI, Claude, Gemini).
Configure agent routing and orchestration logic.
Tune prompts and workflows for operational use cases.

4) Retrieval Augmented Generation (RAG)

Deploy RAG pipelines in customer environments.
Ingest customer documentation and operational knowledge.
Configure knowledge graphs and vector databases.
Tune retrieval pipelines for accuracy and latency.

5) Intelligent Data Agents

Configure SQL agents for structured customer datasets.
Deploy visualization agents for exploratory analytics.
Adapt agents to customer schemas and naming conventions.

6) Explainability & Interpretability

Generate SHAP explanations for model outputs.
Build interpretability reports for engineering stakeholders.
Explain anomaly drivers and optimisation recommendations.
Support trust and adoption of AI insights.

7) Forward Deployment & Customer Integration

Deploy AI systems into restricted industrial networks.
Integrate inference pipelines with:

o Historians
o OPC UA servers
o IoT data streams
o Process control systems

Work with IT/OT teams to satisfy infrastructure and security constraints.
Debug production issues in live operational environments.

8) Production Reliability & MLOps

Monitor inference performance and drift.
Troubleshoot production model failures.
Version models and datasets (DVC or equivalent).
Maintain containerised ML deployments.
Support CI/CD for model updates.

Apply Now

Refine Your Search

network systems operator jobs in karnataka

ML Engineer (Forward Deployed)

AI Platform Engineer

Software Engineering Technical Leader

Staff Site Reliability Operations Engineer

Senior Infrastructure Engineer

Onshore Piping Engineer

Nice- Consultant, Technical Transformation- Sales & Service- Customer

Full Stack Engineer, Forward Deployed

Senior Sales Director

Support Engineer II, Alexa 3PEO

R&D Senior Developer GMS - IDC

Director of Platform, India

AI Infra Architect- Lead Cloud Integrated Infrastructure Engineer II

Regional Solutions Engineer for Asia Pacific South

Software Development Manager, UTR Tech

Shopfloor Apps Solution Architect

Offensive Security / Compromise Assessment - Senior Manager

Sr DevOps Engineer

Developer

Sr. DevOps Manager

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