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Posted on:
3 hours ago
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Job Summary
Job Title: Senior AI/ML Engineer Python (Telecom Products)
Location: Navi Mumbai (On-site)
Experience: 5 12 years (AI/ML backend engineering)
Position Type: Full-time
No. of Positions: 2 3
Product Areas: Lawful Intercept (LI) Location Based Services (LBS) Network
Probes & Monitoring Systems
Location: Navi Mumbai (On-site)
Experience: 5 12 years (AI/ML backend engineering)
Position Type: Full-time
No. of Positions: 2 3
Product Areas: Lawful Intercept (LI) Location Based Services (LBS) Network
Probes & Monitoring Systems
Job Description
We are looking for Senior AI/ML Engineers (Python) to enhance our telecom-
grade products in Lawful Intercept (LI) LBS and network probe systems by
applying machine learning analytics and intelligent automation.
The role focuses on integrating AI/ML into existing high-performance systems
such as traffic classification anomaly detection pattern discovery predictive
analytics and intelligent data enrichment-while respecting regulatory
performance and scalability constraints.
This is not a research-only role. Candidates must be capable of taking models from
concept to production in real-world telecom environments.
We are looking for Senior AI/ML Engineers (Python) to enhance our telecom-
grade products in Lawful Intercept (LI) LBS and network probe systems by
applying machine learning analytics and intelligent automation.
The role focuses on integrating AI/ML into existing high-performance systems
such as traffic classification anomaly detection pattern discovery predictive
analytics and intelligent data enrichment-while respecting regulatory
performance and scalability constraints.
This is not a research-only role. Candidates must be capable of taking models from
concept to production in real-world telecom environments.
Key Responsibilities
1. AI/ML Use-Case Development
Identify and implement AI/ML use cases that enhance LI LBS and probe
products
2. Model Design & Implementation
Design train and validate ML models using real telecom datasets.
Select appropriate algorithms (supervised unsupervised statistical or hybrid)
based on data and system constraints.
Ensure models meet accuracy latency and scalability requirements.
3. Production Integration
Integrate ML models into existing Python / system pipelines.
Optimize models for low-latency inference and high-throughput
processing.
Work closely with core product teams to embed AI features without impacting
system stability.
1. AI/ML Use-Case Development
Identify and implement AI/ML use cases that enhance LI LBS and probe
products
2. Model Design & Implementation
Design train and validate ML models using real telecom datasets.
Select appropriate algorithms (supervised unsupervised statistical or hybrid)
based on data and system constraints.
Ensure models meet accuracy latency and scalability requirements.
3. Production Integration
Integrate ML models into existing Python / system pipelines.
Optimize models for low-latency inference and high-throughput
processing.
Work closely with core product teams to embed AI features without impacting
system stability.
4. Data Engineering & Feature Development
Work with large-scale structured and semi-structured data (IPDR logs
probe outputs location data).
Design and maintain feature extraction pipelines.
Handle noisy incomplete and high-velocity telecom data.
5. Model Monitoring & Lifecycle Management
Implement model monitoring drift detection and retraining strategies.
Validate model behavior under production traffic patterns.
Ensure explainability and traceability where required for regulatory
environments.
6. Collaboration & Knowledge Sharing
Collaborate with backend system QA and DevOps teams.
Participate in design reviews and contribute to AI/ML best practices across
the organization.
Work with large-scale structured and semi-structured data (IPDR logs
probe outputs location data).
Design and maintain feature extraction pipelines.
Handle noisy incomplete and high-velocity telecom data.
5. Model Monitoring & Lifecycle Management
Implement model monitoring drift detection and retraining strategies.
Validate model behavior under production traffic patterns.
Ensure explainability and traceability where required for regulatory
environments.
6. Collaboration & Knowledge Sharing
Collaborate with backend system QA and DevOps teams.
Participate in design reviews and contribute to AI/ML best practices across
the organization.
Mandatory Requirements
Experience:
o 5 12 years of overall experience with strong hands-on work in AI/ML
and Python-based systems.
o Proven experience deploying ML models in production
environments.
Python Expertise:
o Strong proficiency in Python for ML data processing and backend
integration.
ML & Analytics Skills:
o Solid understanding of ML techniques such as:
Classification clustering anomaly detection
Time-series analysis
Statistical and rule-based hybrid approaches
Experience:
o 5 12 years of overall experience with strong hands-on work in AI/ML
and Python-based systems.
o Proven experience deploying ML models in production
environments.
Python Expertise:
o Strong proficiency in Python for ML data processing and backend
integration.
ML & Analytics Skills:
o Solid understanding of ML techniques such as:
Classification clustering anomaly detection
Time-series analysis
Statistical and rule-based hybrid approaches
Data Handling at Scale:
o Experience working with large high-velocity datasets.
o Experience working with large high-velocity datasets.
o Strong skills in data preprocessing feature engineering and validation.
System & Performance Awareness:
o Ability to design ML solutions that respect latency throughput and
resource constraints.
Linux Environment:
o Strong experience working in Linux-based systems.
On-site Requirement:
o Must be based in or willing to relocate to Navi Mumbai and work on-
site.
System & Performance Awareness:
o Ability to design ML solutions that respect latency throughput and
resource constraints.
Linux Environment:
o Strong experience working in Linux-based systems.
On-site Requirement:
o Must be based in or willing to relocate to Navi Mumbai and work on-
site.
Preferred Skills (Strong Advantage)
Telecom Domain Experience:
o Prior experience with LI IPDR LBS probes mediation or telecom
analytics systems.
ML Frameworks:
o Hands-on experience with scikit-learn TensorFlow PyTorch or
similar frameworks.
Telecom Domain Experience:
o Prior experience with LI IPDR LBS probes mediation or telecom
analytics systems.
ML Frameworks:
o Hands-on experience with scikit-learn TensorFlow PyTorch or
similar frameworks.
Streaming & Real-Time Processing:
o Experience working with real-time or near-real-time data pipelines.
Deployment & MLOps:
o Familiarity with model deployment monitoring and CI/CD
pipelines.
o Experience with containerized ML services (Docker / Kubernetes) is a
plus.
o Experience working with real-time or near-real-time data pipelines.
Deployment & MLOps:
o Familiarity with model deployment monitoring and CI/CD
pipelines.
o Experience with containerized ML services (Docker / Kubernetes) is a
plus.
