AI Scientist

Beetexting; Hyderabad; On-site; INR 4,500,000 - 8,000,000; Full time

Job summary

Beetexting is seeking an AI Scientist to lead end-to-end AI/ML initiatives, including Generative AI, LLMs, and autonomous agents, in production environments. You will architect scalable pipelines, guide model design from CNNs to transformers, and drive responsible AI practices across the stack.

The role requires advanced degrees and hands-on experience deploying production-grade AI/ML solutions using PyTorch, TensorFlow, and Hugging Face, with strong coding in Python/C++.

Qualifications

• Master’s or PhD in AI/ML or related field.
• Hands-on experience in Generative AI and Deep Learning.
• Proficient in Python, C++, Java; strong testing and performance profiling.
• Extensive experience with PyTorch, TensorFlow, and Hugging Face.

Responsibilities

• Spearhead end-to-end development, deployment, and optimization of AI/ML systems (Generative AI, LLMs, RAG, autonomous agents).
• Architect scalable Retrieval-Augmented Generation pipelines and multi-modal transformers.
• Lead deep-learning model design: CNN, RNN, Transformers, GANs, Diffusion models; MoE architectures.
• Define best practices for prompt engineering, fine-tuning, quantization, pruning, distillation, and inference optimization.
• Build and maintain MLOps pipelines: CI/CD, model versioning, Docker/GPU, Kubernetes, IaC.
• Design high-performance data workflows: ETL, streaming, caching; Redis, Kafka.
• Ensure secure, compliant AI deployments with Responsible AI, governance, and regulatory controls.
• Monitor emerging research and integrate top innovations into production.
• Collaborate with Product, DevOps, Data Engineering, and Security; mentor engineers.

Skills

Python

C++

Java/Spring Boot

PyTorch

TensorFlow

LLMs

MLOps

CI/CD

Model deployment

Education

Master’s or PhD in AI/ML

Tools

LangChain

AutoGPT

BabyAGI

NVIDIA NeMo

Pinecone

Milvus

Chroma

Quadrant

MLflow

Kubeflow

Docker

Kubernetes

Job description

Hands-on experience in AI/ML roles, Generative AI and Deep Learning

Join Beetexting - Revolutionizing Communication with Cutting-Edge Technology!

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 positive
• We 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.

Position: AI Scientist

Key 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 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: Master’s or PhD in AI/ML, Computer Science (AI/ML specialization), Data Science, Mathematics, or related field
• Industry Experience: Experience in AI/ML roles, including hands-on in Generative AI and Deep Learning
• 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.