AI/ML Lead Engineer

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