Bangalore, Karnataka, India
Information Technology
Full-Time
Galaxy Office Automation Pvt. Ltd.
Overview
Employment Type: Full-Time
Compensation: Competitive; based on experience and capability
Role Summary : We’re looking for a Data Scientist / AI Engineer / Machine Learning Engineer who can own systems end to end and contribute from the ground up . In this role, you’ll architect and deploy real-world AI solutions using LLMs, predictive modeling, multimodal intelligence , and FastAPI-based microservices . You’ll work across core platform modules and client-specific projects—bridging the gap between cutting-edge AI and enterprise-grade deployment.
What You’ll Work On
Compensation: Competitive; based on experience and capability
Role Summary : We’re looking for a Data Scientist / AI Engineer / Machine Learning Engineer who can own systems end to end and contribute from the ground up . In this role, you’ll architect and deploy real-world AI solutions using LLMs, predictive modeling, multimodal intelligence , and FastAPI-based microservices . You’ll work across core platform modules and client-specific projects—bridging the gap between cutting-edge AI and enterprise-grade deployment.
What You’ll Work On
- Multi-Agent Collaboration, Reasoning, Memory & Human Alignment Build intelligent agents and swarms with multi-agent collaboration, reasoning, planning, memory, and alignment with human feedback. Use protocols such as MCP and A2A and frameworks such as LangChain and Crew AI .
- Retrieval-Augmented Generation (RAG) Develop hybrid pipelines using vector databases (FAISS, Qdrant, Pinecone) and transformer-based generation models
- Multimodal AI (Language + Vision + Audio + Video) Build systems that process and combine intelligence across language, vision, audio, and video.
- Predictive Modeling Build predictive pipelines using deep learning architectures (e.g., LSTMs, CNNs, RNNs), transformer-based models (e.g. openAI, LLama, Qwen, Mistral) , and ensemble methods (e.g., XGBoost, LightGBM, Random Forests). Emphasize modeling depth, generalization, interpretability, maintainability and dynamic improvements.
- Conversational Assistants Develop conversational assistants with advanced capabilities such as model based recommendations, user-query based what-if scenario analyses and continuous improvements based on memory and human feedback.
- FastAPI-Based Backend APIs Wrap agents and models into versioned, secure, and production-grade FastAPI microservices .
- Model Lifecycle Management Track, evaluate, and manage model lifecycles using MLflow, DVC, and internal governance tools.
- Data Engineering & Integration Ingest and transform data from SQL and NoSQL (e.g. MongoDB) sources, APIs, and distributed pipelines.
- 1–4 years of experience in AI/ML product development or applied data science
- Strong Python skills: Pandas, NumPy, scikit-learn, Transformers, PyTorch/TensorFlow
- Hands-on experience with LLMs (OpenAI, Mistral, Claude, Llama, Deepseek, Gemini, etc.), LangChain, prompt engineering, and integration with real-world use cases
- Proven experience building agentic systems, including reasoning agents and multi-agent collaboration
- Deep expertise in predictive modeling using transformers, deep learning and ensemble methods
- Familiarity with image, audio, and video model development
- Familiarity with model monitoring and re-training
- Exposure to both SQL and NoSQL databases
- Experience building and deploying Python based backend APIs using FastAPI
- Proficiency with Git workflows, CI/CD, and modular code development
- Strong communication, documentation, and architectural thinking
- Candidates with experience in reinforcement learning (including RLHF) will be preferred.
- Prior exposure to supervised fine-tuning (SFT) and parameter-efficient tuning approaches such as LoRA and QLoRA is desirable.
- Familiarity with workflow orchestration tools like Airflow, Celery, Prefect, or Dagster will be advantageous.
- Experience building autoscaling and serverless architectures using AWS Lambda, ECS, or EKS is a plus.
- Candidates with an expertise in backend engineering with a focus on microservice optimization will be preferred.
- Candidates with experience in containerization and orchestration using Docker and Kubernetes will be valued.
- Hands-on knowledge of designing conversational UI or chatbot pipelines is beneficial.
- Prior experience working with big data tools like Spark, Hive, or Hadoop is desirable.
- Experience deploying AI systems in BFSI, healthcare, e-commerce, or government environments will be an added advantage.
- End-to-End Ownership from design to deployment
- Agent-First Ecosystem Exposure
- Product + Custom Work across client and platform needs
- Leadership Track into applied AI architecture
- Live Enterprise Impact across sectors
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