Ai ML Lead (Hea Job/ 287)

Title: Lead AI-ML Engineer / AI-ML Technical Lead
Role/Level: AI-ML Lead Engineer / Expert
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
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.
 AI/ML Strategy & Technical / Product Roadmap:
o Define the organizational AI/ML roadmap aligned to product lines, program
requirements, and long-term technology strategy
o Identify strategic opportunities for AI/ML insertion into existing and future defense
programs
o Establish ML maturity models, capability benchmarks, and program-level AI readiness
assessments
o Engage with government customers, program offices, and stakeholders to align AI/ML
direction with mission requirements, translate operational mission gaps into wellscoped,
solvable ML problem statements
o 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:
o Lead end-to-end execution of ML projects: data acquisition → feature engineering →
model training → evaluation → deployment → monitoring
Job Description
Role Summary
Key Responsibilities
o Apply MLOps best practices including experiment tracking, model versioning, CI/CD for
ML pipelines, and automated regression testing
o Ensure models meet defense-specific requirements: explainability, robustness,
adversarial resilience, and compliance with AI ethics policies (DoD AI Ethics Principles)
o Deliver models that operate within embedded and edge compute constraints (VPX,
Jetson, FPGA-adjacent deployments)
o Manage technical risk, communicate status to program leadership, and drive mitigation
strategies when needed
o Design and build autonomous ML agents for mission-critical workflows including ISR
(Intelligence, Surveillance, Reconnaissance), SIGINT analysis, target recognition, and
autonomous decision support
o Implement retrieval-augmented generation (RAG) pipelines for intelligence analysis and
sensor data interpretation workflows
 ML Team Development
o Define ML engineering roles, competency frameworks, and career ladders for the AI/ML
team, along with senior leadership
o Lead technical screening, interview design, and hiring decisions for ML engineers, data
scientists, and MLOps engineers
o Build a team culture of rigorous experimentation, reproducibility, and defense-grade
engineering discipline
o Mentor junior and mid-level engineers through structured technical development plans
Supervised / Unsupervised
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
Primary Skills (Must Have)
Model Monitoring Drift detection, performance degradation alerting, data quality
monitoring in production
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
• 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
• NVIDIA Triton Inference Server — scalable model serving for multi-model deployments
• Hugging Face ecosystem — model hub, Transformers library, PEFT/LoRA for efficient finetuning
• 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
• 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
Secondary Skills (Good to Have)
Soft Skills