About Job/Role

Aera Technology is the Decision Intelligence company. We deliver innovation and services that enable enterprises to operate sustainably, intelligently, and efficiently. Our platform, Aera Decision Cloud™ , integrates with your existing systems to digitize, augment, and automate decisions in real time.
We help enterprises around the world transform decision making – delivering millions of recommendations that have resulted in significant revenue gains and cost savings for some of the world’s best-known brands.
We are seeking a Machine Learning Engineer with expertise in Large Language Models (LLMs) and Agentic AI to join our Pune team. In this role, you will design and implement cutting-edge ML features that power data science at scale and enable autonomous, intelligent decision-making for enterprise clients.

Roles and Responsibilities

Design and implement state-of-the-art Machine Learning and LLM-powered features for the Aera Platform.
Build and optimize agentic AI workflows that enable autonomous decision-making and multi-step reasoning.
Solve problems at scale and develop fault-tolerant, production-grade systems.
Operationalize data science by integrating ML models into robust pipelines, with a focus on LLM orchestration, fine-tuning, and inference optimization.
Develop and maintain distributed systems, abstractions, model hosting, inference pipelines, and serverless infrastructure.
Collaborate closely with Data Science, Engineering, and DevOps teams to deliver scalable and maintainable solutions.
Explore, evaluate, and integrate emerging AI technologies to push the boundaries of autonomous agents, retrieval-augmented generation (RAG), and LLM-based reasoning.

Required Qualification

B.E./ B.Tech in Computer Science, Computer Engineering, or related field.
3–5 years of experience in software engineering and architecture.
Proven ability in designing high-quality, scalable, and maintainable systems (strong system design skills).
At least 2 years of experience in designing and deploying Machine Learning or LLM-based systems (6 months to a year for LLM).
Strong Python skills; experience with libraries such as FastAPI, Flask, LangChain, LlamaIndex, or equivalent.
Experience working with large datasets, ML pipelines, and distributed systems (e.g., Ray, Spark).
Experience building and fine-tuning ML/LLM models using Hugging Face, PyTorch, TensorFlow, scikit-learn, pandas, etc.
Strong skills in containerized microservices (Docker, Kubernetes) and CI/CD tools (Jenkins, Git, Jira).
Excellent problem-solving, communication, and collaboration skills.

Good to Have

Experience with GoLang for high-performance components.
Familiarity with vector databases (Pinecone, Weaviate, FAISS).
Knowledge of event streaming and caching technologies (Kafka, Redis).
Exposure to ML engineering tools such as MLflow, Data Version Control (DVC), and experiment tracking systems.
Understanding of multi-modal AI and integrating text, image, and structured data in AI workflow
Experience in designing autonomous agents, RAG pipelines, and multi-step reasoning systems.
Familiarity with serverless AI infrastructure and cloud providers (AWS, GCP, Azure).

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