Job description

Job Description

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

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:

Lead end-to-end execution of ML projects: data acquisition → feature engineering →model training → evaluation → deployment → monitoring
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)

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

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

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
NVIDIA Triton Inference Server — scalable model serving for multi-model deployments
Hugging Face ecosystem — model hub, Transformers library, PEFT/LoRA for efficient fine-tuning
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

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