AI Lead

About Us :   Beetexting is a growth stage and VC-backed SaaS company headquartered in the USA with a cutting-edge development center in Hyderabad, India. We are on a mission to build next-gen software products that transform communication with AI. Our team thrives on innovation, passion, and a relentless pursuit of excellence. We are looking for code lovers who are excited to develop groundbreaking products and make a real impact in the market.Why Join Beetexting?  We are entrepreneurs. We’re resourceful and positiveWe are innovators. We obsess on helping our customer win.We are visionaries. We think deeply about the industry and think way, way ahead.We are beelievers. We’re humble. We believe in each other and support one another.We are givers. We think of others first.We are intentional. We plan.We are diligent. We measure outcomes and stay focused on the profitable.We are gritty. We execute and we never give up.Role Details : Position :  AI LeadLocation :   Hyderabad, Telangana, IndiaKey Responsibilities :   Spearhead the end-to-end development, deployment, and continuous optimization of cutting-edge AI / ML systems—Generative AI, LLMs / MLLMs, RAG, autonomous agents, and custom MCP servers in productionArchitect 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 architecturesDefine and enforce best practices for prompt engineering, fine-tuning (LlamaIndex, Hugging Face), quantization (INT4, GGUF), pruning, distillation, and advanced inference optimizationsBuild 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 codeDesign 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 productionCollaborate closely with Product, DevOps, Data Engineering, and Security teams; mentor and guide engineers and data scientists in software engineering and model validation best practicesRequired Qualifications :   Education :   Master’s or PhD in AI / ML, Computer Science (AI / ML specialization), Data Science, Mathematics, or related fieldIndustry Experience :  15+ years in AI / ML roles, including 7–10 years hands-on in Generative AI and Deep Learning, plus 5+ years in senior or leadership positionsHands-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 disciplineDeep 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, QuadrantAdvanced 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 mathComputer Vision & Document Processing :  Expertise in OpenCV, PyMuPDF, python-docx / pptx, pytesseract for advanced text / image extraction and analysisCloud & Infrastructure :  GPU acceleration (CUDA, cuDNN); AWS (Bedrock, SageMaker), Azure AI Studio & GPU Containers, GCP (Vertex AI, Cloud Run), serverless AI, NVIDIA Triton Inference ServerBackend & APIs :  FastAPI, OpenAPI, Django REST Framework, Flask for building and scaling AI microservicesData Engineering & Storage :   ETL pipelines, streaming, caching; relational (MySQL, Oracle SQL), NoSQL (MongoDB), vector DBsMLOps & Observability :  MLflow, Kubeflow; Docker, Kubernetes, OpenShift; CI / CD (Git, Bitbucket); monitoring with Prometheus, Grafana, OpenTelemetry, Logstash, KibanaSecurity & Compliance :   Secure coding, network security, and adherence to GDPR, HIPAA, GxP standardsIndustry 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 frameworksSignificant open-source contributions to major AI / ML projectsProven 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!Apply here or email to