AI/ML Lead Engineer

Keka Technologies Private Limited; Bengaluru; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

Keka Technologies Private Limited is seeking a Lead AI-ML Engineer / AI-ML Technical Lead to define and execute a comprehensive AI/ML strategy across defense and mission-critical products.

The role combines hands-on development with leadership, guiding a team to build, deploy, and certify ML models under real-world constraints such as SWaP-C, latency, and certification standards.

Qualifications

• 10-12 years of hands-on ML/AI development experience; >2+ years in a technical lead or architect role
• Proven track record of taking ML systems from research through fielded deployment
• Hands-on leadership with ability to build and mentor a team across defense-focused AI programs

Responsibilities

• Define the organizational AI/ML roadmap aligned to product lines and mission requirements
• Lead ML strategy, feasibility assessments, and performance criteria for defense programs
• Deliver end-to-end ML projects from concept to fielded capability under SWaP-C constraints
• Apply MLOps, CI/CD, model versioning, and automated testing for production pipelines
• Manage risk, report status to program leadership, and drive mitigation strategies
• Design autonomous ML agents for ISR, SIGINT, target recognition, and decision support
• Implement retrieval-augmented generation (RAG) pipelines for intelligence analysis

Skills

Deep Learning

Reinforcement Learning

Object Detection

Explainable AI

Model Compression

Edge Deployment

Data Pipeline

MLOps

Team Leadership

Research to Deployment

Education

BE/B.Tech, M.E./M.Tech/MCA/MS or PhD in CS/EE/Applied Mathematics or related field

Tools

Python

PyTorch

TensorFlow

Keras

Scikit-learn

MLflow

Weights & Biases

CUDA

cuDNN

TensorRT

ONNX Runtime

Job description

Title: Lead AI-ML Engineer / AI-ML Technical Lead

Domain: Mission Critical Products

Employment: Full-Time, Permanent

Qualifications

• BE/B. Tech, M.E/M. Tech/MCA/ MS or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or related field
• Experience: 10-12 years of hands-on ML/AI development experience; >2+ years in a technical lead or architect role
• Proven track record of taking ML systems from research through fielded deployment

Role Summary

We are seeking a seasoned Senior AI/ML Developer and Lead to define, build, and drive the AI/ML strategy across our portfolio of defense and mission-critical products. This is a hands-on leadership role — you will craft the organizational AI/ML roadmap, identify and formalize problem statements, build and mentor the team, and personally execute complex ML projects from concept to fielded

capability. You will apply rigorous engineering discipline and industry-standard AI/ML practices to real-world defense challenges including radar signal processing, autonomous systems, sensor fusion, anomaly detection, and intelligence analysis. You will be responsible for designing and deploying ML agents and AI pipelines that operate within the constraints of SWaP-C (Size, Weight, Power, and Cost), real-time processing, and defense certification standards.

Key Responsibilities

AI/ML Strategy & Technical / Product Roadmap

• Define the organizational AI/ML roadmap aligned to product lines, program requirements, and long-term technology strategy
• Identify strategic opportunities for AI/ML insertion into existing and future defense programs
• Establish ML maturity models, capability benchmarks, and program-level AI readiness assessments
• Engage with government customers, program offices, and stakeholders to align AI/ML direction with mission requirements, translate operational mission gaps into well-scoped, solvable ML problem statements
• Conduct technical feasibility assessments — data availability, model complexity ,latency requirements, computational constraints, Define success criteria, performance metrics (Pd, Pfa, F1, mAP, latency SLAs), and evaluation frameworks for each problem

ML Project Execution

• Apply MLOps best practices including experiment tracking, model versioning, CI/CD for ML pipelines, and automated regression testing
• Ensure models meet defense-specific requirements: explain ability, robustness, adversarial resilience, and compliance with AI ethics policies (DoD AI Ethics Principles)
• Deliver models that operate within embedded and edge compute constraints (VPX,Jetson, FPGA-adjacent deployments)
• Manage technical risk, communicate status to program leadership, and drive mitigation strategies when needed
• Design and build autonomous ML agents for mission-critical workflows including ISR (Intelligence, Surveillance, Reconnaissance), SIGINT analysis, target recognition, and autonomous decision support
• Implement retrieval-augmented generation (RAG) pipelines for intelligence analysis and sensor data interpretation workflows

ML Team Development

• Define ML engineering roles, competency frameworks, and career ladders for the AI/ML team, along with senior leadership
• Lead technical screening, interview design, and hiring decisions for ML engineers, data scientists, and MLOps engineers
• Build a team culture of rigorous experimentation, reproducibility, and defense-grade engineering discipline
• Mentor junior and mid-level engineers through structured technical development plans

Primary Skills (Must Have)

Learning- Deep expertise in classification, regression, clustering, anomaly detection — implemented and deployed, not just academic

Deep Learning -CNNs, RNNs/LSTMs, Transformers — architecture design, training from scratch, fine-tuning

Reinforcement Learning Policy-based methods (PPO, SAC) for autonomous agent and decision support applications

Object Detection / Tracking YOLO, DETR, Faster R-CNN; multi-object tracking algorithms (SORT, ByteTrack, DeepSORT)

Explainable AI (XAI) SHAP, LIME, attention visualization — critical for defense decisions requiring human interpretability

Model Compression Quantization, pruning, knowledge distillation for embedded/edge deployment under SWaP-C constraints

Tools & Frameworks Python / PyTorch / TensorFlow / Keras / Scikit-learn / MLflow /Weights & Biases / CUDA / cuDNN

ML Agent & Agentic AI Tools LangChain / LangGraph / RAG Frameworks / LLM Integration / AutoGen/ CrewAI

Edge Deployment TensorRT, ONNX Runtime, OpenVINO — model optimization for embedded targets (Jetson, VPX, x86 embedded)

Data Pipeline Engineering Apache Kafka, Airflow, or equivalent for high-throughput sensor data ingestion and processing

Secondary Skills (Good to Have)

• Knowledge of defense system integration: VPX/OpenVPX architectures, real-time OS(RTOS), embedded Linux environments
• Experience with graph neural networks (GNNs) for multi-sensor fusion, network analysis, or knowledge graph applications in defense
• Synthetic data generation — GANs, diffusion models, or simulation-based data augmentation for rare event training (critical in defense where real data is scarce)
• MATLAB / Simulink — for radar simulation, signal modeling, and integration with existing defense engineering workflows
• Label Studio / CVAT — data annotation tooling and annotation pipeline management for building ground truth datasets
• Electronic warfare: emitter classification, jamming detection, spectrum monitoring using ML
• Computer vision for EO/IR (Electro-Optical/Infrared) — thermal imagery, multi-spectral fusion
• Natural language processing (NLP) applied to OSINT, intelligence report analysis, or operator decision support

Soft Skills

• Excellent communication skills. The candidate will take part in problem validation, Solution architecture/ Design reviews with customer/stakeholders.
• Result oriented and Team Player attitude
• Ability to adapt to changing environment
• Flair for continuous improvement through automation, simulation leading to increased productivity
• Adapt agile process/methodologies as needed
• Openness to work across programs/products/teams

Required Skills

python cuDNN autogen keras regression, clustering, anomalydetection SORT,ByteTrack, DeepSORT) Lang chain MLFLOW -CNNs, RNNs/LSTMs,