AI Lead

Role Details:

Position:

Location:

Key Responsibilities:

• Spearhead the end-to-end development, deployment, and continuous optimization of cutting-edge AI/ML systemsGenerative AI, LLMs/MLLMs, RAG, autonomous agents, and custom MCP servers in production
• Architect and implement scalable Retrieval-Augmented Generation pipelines, multi-modal transformer systems, and autonomous multi-agent frameworks (LangChain, AutoGPT, BabyAGI, NVIDIA NeMo Agents)
• Lead deep-learning model design and delivery: ANN, CNN, RNN, LSTM/BiLSTM, attention-based Transformers (BERT, GPT, T5, Vision Transformers), GANs, VAEs, Diffusion models, and Mixture-of-Experts architectures
• Define and enforce best practices for prompt engineering, fine-tuning (LlamaIndex, Hugging Face), quantization (INT4, GGUF), pruning, distillation, and advanced inference optimizations
• Build and maintain robust MLOps pipelines: CI/CD (Git, Bitbucket, automated tests), model versioning (MLflow, Hugging Face Hub & LFS), Docker/GPU containerization, Kubernetes/OpenShift orchestration, Terraform infrastructure as code
• Design high-performance data engineering workflows: ETL optimization (Pandas, NumPy, Spark), real-time streaming (Kafka, RabbitMQ), and caching strategies (Redis)
• Ensure secure, compliant AI deployments by embedding Responsible AI principles (bias mitigation, transparency, privacy), governance frameworks, and regulatory controls.
• Monitor emerging research (self-supervised pre-training, federated learning, synthetic data, LLMOps) and integrate top innovations into production
• Collaborate closely with Product, DevOps, Data Engineering, and Security teams; mentor and guide engineers and data scientists in software engineering and model validation best practices

Required Qualifications:

• Education:

  Masters or PhD in AI/ML, Computer Science (AI/ML specialization), Data Science, Mathematics, or related field

• Industry Experience:

  15+ years in AI/ML roles, including 710 years hands-on in Generative AI and Deep Learning, plus 5+ years in senior or leadership positions

• Hands-On Coding & Deployment:

  Expert at architecting, writing, debugging, deploying, and optimizing production-grade AI/ML code in Python (primary), C++, R, Java/Spring Boot, with strong testing, code-review, and performance-profiling discipline

• Deep Learning & Generative AI Frameworks:

  Advanced usage of PyTorch, TensorFlow, ONNX, GGUF, Hugging Face Transformers & Diffusers; fine-tuning pipelines (LlamaIndex, custom scripts)

• Large Language & Multimodal Models:

  Production deployment of LLMs, Multimodal LLMs (vision-language models, cross-modal understanding), RAG systems, and custom MCP A2A servers; embedding management & vector search with Pinecone, Milvus, Chroma, Quadrant

• Advanced Model Optimization:

  Quantization, pruning, distillation, MoE routing, efficient inference strategies, prompt-engineering platforms (LangSmith, PromptFlow)

• Classical & Advanced ML:

  Proficiency in regression, decision trees, random forests, SVM; boosting (XGBoost, CatBoost, LightGBM); clustering (K-Means, DBSCAN, hierarchical); RL (Q-Learning, DDPG, PPO); statistical methods and optimization math

• Computer Vision & Document Processing:

  Expertise in OpenCV, PyMuPDF, python-docx/pptx, pytesseract for advanced text/image extraction and analysis

• Cloud & Infrastructure:

  GPU acceleration (CUDA, cuDNN); AWS (Bedrock, SageMaker), Azure AI Studio & GPU Containers, GCP (Vertex AI, Cloud Run), serverless AI, NVIDIA Triton Inference Server

• Backend & APIs:

  FastAPI, OpenAPI, Django REST Framework, Flask for building and scaling AI microservices

• Data Engineering & Storage:

  ETL pipelines, streaming, caching; relational (MySQL, Oracle SQL), NoSQL (MongoDB), vector DBs

• MLOps & Observability:

  MLflow, Kubeflow; Docker, Kubernetes, OpenShift; CI/CD (Git, Bitbucket); monitoring with Prometheus, Grafana, OpenTelemetry, Logstash, Kibana

• Security & Compliance:

  Secure coding, network security, and adherence to GDPR, HIPAA, GxP standards

• Industry Certifications & Research:

  Must hold at least one recognized AI/ML certification (e.g. AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate) and have published research papers in top-tier AI/ML conferences or journals (NeurIPS, ACM, arXiv, AAAI, JMLR).

Good to Have Skills:

• PhD-level publications and advanced certifications (Stanford AI, MIT, AWS, Google)
• Experience with federated learning, synthetic data generation, privacy-preserving ML (differential privacy, homomorphic encryption), and edge/in-device inference (TensorFlow Lite, CoreML)
• Familiarity with Responsible AI toolkits, model cards/DataCard’s, and AI risk management frameworks
• Significant open-source contributions to major AI/ML projects
• Proven success integrating AI in regulated industries (Texting/Messaging, pharma, healthcare, finance) Industries and disciplines.

How to Apply:

If you are a passionate developer who loves coding and thrives in an innovative startup environment, apply now to join our journey in revolutionizing communication!

join@beetexting.com