Senior Applied Scientist - AI Agent Systems

Senior Applied Scientist - AI Agent Systems at Auxo AI · Bengaluru (Bangalore), Mumbai, Hyderabad, Delhi, Gurugram · 3 - 10 years · ₹20L - ₹40L / yr · Raised funding · Posted 7 Sep 2026

Senior Applied Scientist - AI Agent Systems

at Auxo AI

Posted by Anupam Arya

3 - 10 yrs

₹20L - ₹40L / yr

Bengaluru (Bangalore), Mumbai, Hyderabad, Delhi, Gurugram

Skills

Machine Learning (ML)

Retrieval Augmented Generation (RAG)

Agentic AI

AuxoAI is hiring a Senior Applied Scientist to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.

This role focuses on building reasoning and decision systems using planning algorithms, search methods, and optimization techniques, rather than chatbot or RAG-style application development. The ideal candidate will design intelligent agent architectures that combine LLM-based reasoning with classical planning, search algorithms, and optimization techniques, operating reliably in real-world environments with constraints around latency, cost, uncertainty, and limited context windows.

You will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems.

You will also work on problems where existing architectures may not be sufficient, and will be expected to experiment with new approaches that combine machine learning, graph algorithms, and classical AI techniques to build reliable, production-grade systems.

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities:

Design and architect modular AI agent frameworks incorporating skill decomposition, tool orchestration, and persistent state tracking.
Implement planning and search algorithms such as Monte Carlo Tree Search (MCTS), beam search, A search, heuristic search, and graph-based planning approaches* to support complex decision-making tasks.
Develop decision-making loops that balance trade-offs between exploration vs. exploitation, cost vs. accuracy, and latency vs. reasoning depth.
Build structured memory systems including episodic memory stores, semantic memory layers, and vector-based memory with optimized retrieval strategies.
Design tool-calling architectures with strong execution validation, retry mechanisms, and failure recovery strategies.
Develop evaluation frameworks to measure agent performance using task success metrics, rollout simulations, and multi-sample validation approaches.
Improve agent performance through techniques such as distillation, synthetic trajectory generation, prompt compression, and context pruning.
Deliver production-ready agent systems that meet operational requirements around reliability, cost efficiency, throughput, and observability.

Requirements

3-10 years of experience building machine learning or AI systems in production environments.
Strong experience implementing search or planning algorithms beyond basic use cases, including tree search or heuristic-based planning approaches.
Hands-on experience with Monte Carlo Tree Search (MCTS) or related decision-making frameworks.
Strong understanding of state-space representations, heuristic design, and decision boundary trade-offs.
Experience building or extensively customizing agent frameworks for real-world applications.
Hands-on experience designing tool-use or function-calling architectures under practical system constraints.
Strong Python engineering skills with a focus on scalable and reliable system design.

Candidates whose primary experience is limited to RAG pipelines, prompt engineering, or chatbot frameworks without deeper algorithmic or systems work may not be a fit for this role.

Nice to Have:

Experience with reinforcement learning techniques such as policy gradients, value estimation, or reward modeling.
Experience building multi-agent or collaborative agent systems.
Experience designing evaluation frameworks for agent robustness and reliability.
Experience optimizing LLM inference pipelines for latency, throughput, and cost efficiency.
Familiarity with distributed task orchestration systems and large-scale AI workflow management.

AuxoAI is hiring a Senior Applied Scientist to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.

You will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems.

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities:

Design and architect modular AI agent frameworks incorporating skill decomposition, tool orchestration, and persistent state tracking.
Implement planning and search algorithms such as Monte Carlo Tree Search (MCTS), beam search, A search, heuristic search, and graph-based planning approaches* to support complex decision-making tasks.
Develop decision-making loops that balance trade-offs between exploration vs. exploitation, cost vs. accuracy, and latency vs. reasoning depth.
Build structured memory systems including episodic memory stores, semantic memory layers, and vector-based memory with optimized retrieval strategies.
Design tool-calling architectures with strong execution validation, retry mechanisms, and failure recovery strategies.
Develop evaluation frameworks to measure agent performance using task success metrics, rollout simulations, and multi-sample validation approaches.
Improve agent performance through techniques such as distillation, synthetic trajectory generation, prompt compression, and context pruning.
Deliver production-ready agent systems that meet operational requirements around reliability, cost efficiency, throughput, and observability.

Requirements

3-10 years of experience building machine learning or AI systems in production environments.
Strong experience implementing search or planning algorithms beyond basic use cases, including tree search or heuristic-based planning approaches.
Hands-on experience with Monte Carlo Tree Search (MCTS) or related decision-making frameworks.
Strong understanding of state-space representations, heuristic design, and decision boundary trade-offs.
Experience building or extensively customizing agent frameworks for real-world applications.
Hands-on experience designing tool-use or function-calling architectures under practical system constraints.
Strong Python engineering skills with a focus on scalable and reliable system design.

Candidates whose primary experience is limited to RAG pipelines, prompt engineering, or chatbot frameworks without deeper algorithmic or systems work may not be a fit for this role.

Nice to Have:

Experience with reinforcement learning techniques such as policy gradients, value estimation, or reward modeling.
Experience building multi-agent or collaborative agent systems.
Experience designing evaluation frameworks for agent robustness and reliability.
Experience optimizing LLM inference pipelines for latency, throughput, and cost efficiency.
Familiarity with distributed task orchestration systems and large-scale AI workflow management.

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About Auxo AI

Founded :

2022

Type :

Services

Size :

100-1000

Stage :

Raised funding

About

N/A

Company social profiles

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Experience with data engineering and pipelines.
A history of side projects, open source contributions, or products you shipped end-to-end.
Experience working at a consulting or professional services firm in a client-facing delivery role.

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Experience with on-premises or private cloud (VPC) deployments.
Experience deploying AI systems in regulated industries such as insurance, banking, or the public sector.
Experience with data engineering and pipelines.
A history of side projects, open source contributions, or products you shipped end-to-end.
Experience working at a consulting or professional services firm in a client-facing delivery role.

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Role Overview

Key Responsibilities

Write clean, tested, production-grade code and participate actively in code and design reviews.

Required Qualifications

8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs.

Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel).

Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers.

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relevance, and reliability.

Develop and integrate MCP-based tools and services for AI agents.

Work with LLMs, SLMs, quantized models, and model optimization

techniques for efficient inference.

Develop scalable backend services and APIs for AI applications.

Design databases and data models supporting AI/agentic applications.

Implement AI observability covering latency, token usage, cost, failures,

quality, and agent/tool execution.

Apply AI governance and responsible AI practices, including security,

access control, data privacy, and auditability.

Optimize AI systems for latency, scalability, cost, and reliability.

Collaborate with engineering and product teams to take AI solutions from

POC to production.

Strong hands-on experience with GenAI, LLMs, and Agentic AI.

Experience building RAG applications.

Strong understanding of Context Engineering and prompt/context

optimization.

Role Overview

We are looking for a hands-on AI/ML Engineer to design, develop, and deploy

production-ready GenAI and Agentic AI applications. The role involves building

intelligent agents, RAG pipelines, AI APIs, backend services, and scalable AI

infrastructure with a strong focus on context engineering, observability,

governance, and model optimisation.

Key Responsibilities

Required Skills

Practical experience with MCP (Model Context Protocol).

Experience with frameworks such as LangChain, LangGraph,

LlamaIndex, or equivalent.

Knowledge of LLM/SLM deployment and quantization techniques.

Strong Python backend development experience.

Experience developing REST APIs using FastAPI/Flask or equivalent.

Strong understanding of SQL/NoSQL databases and database design.

Experience with vector databases such as Qdrant, Pinecone, Weaviate,

ChromaDB, or FAISS.

Understanding of AI observability, evaluation, monitoring, and

governance.

Experience with cloud platforms and production deployment is preferred.

Strong understanding of software engineering principles, Git, testing, and

CI/CD.

Design and develop Agentic AI systems using LLMs, tools, memory,

workflows, and MCP.

Build production-grade RAG pipelines, including ingestion, chunking,

embeddings, retrieval, reranking, and evaluation.

Implement context engineering strategies for improving LLM accuracy,

relevance, and reliability.

Develop and integrate MCP-based tools and services for AI agents.

Work with LLMs, SLMs, quantized models, and model optimization

techniques for efficient inference.

Develop scalable backend services and APIs for AI applications.

Design databases and data models supporting AI/agentic applications.

Implement AI observability covering latency, token usage, cost, failures,

quality, and agent/tool execution.

Apply AI governance and responsible AI practices, including security,

access control, data privacy, and auditability.

Optimize AI systems for latency, scalability, cost, and reliability.

Collaborate with engineering and product teams to take AI solutions from

POC to production.

Strong hands-on experience with GenAI, LLMs, and Agentic AI.

Experience building RAG applications.

Strong understanding of Context Engineering and prompt/context

optimization.

Role Overview

We are looking for a hands-on AI/ML Engineer to design, develop, and deploy

production-ready GenAI and Agentic AI applications. The role involves building

intelligent agents, RAG pipelines, AI APIs, backend services, and scalable AI

infrastructure with a strong focus on context engineering, observability,

governance, and model optimisation.

Key Responsibilities

Required Skills

Practical experience with MCP (Model Context Protocol).

Experience with frameworks such as LangChain, LangGraph,

LlamaIndex, or equivalent.

Knowledge of LLM/SLM deployment and quantization techniques.

Strong Python backend development experience.

Experience developing REST APIs using FastAPI/Flask or equivalent.

Strong understanding of SQL/NoSQL databases and database design.

Experience with vector databases such as Qdrant, Pinecone, Weaviate,

ChromaDB, or FAISS.

Understanding of AI observability, evaluation, monitoring, and

governance.

Experience with cloud platforms and production deployment is preferred.

Strong understanding of software engineering principles, Git, testing, and

CI/CD.

Full stack software and AI Agent developer

Posted by Amit Singh

Remote only

3 - 15 yrs

₹6L - ₹12L / yr (ESOP available)

Python

React.js

Position Overview

We are seeking a versatile Senior Full Stack & AI Agent Developer to architect, build, and

maintain end-to-end software solutions spanning web platforms, desktop applications, and

autonomous AI agents capable of interacting with and controlling these software systems.

The ideal candidate will bridge traditional engineering software with cutting-edge artificial

intelligence to automate data processing and enhance operational decision-making. While

not strictly required, a background or strong interest in the energy sector—specifically

drilling and completion operations—is highly desirable.

Key Responsibilities

• Full Stack Development: Design, develop, and deploy robust web applications and

native desktop software utilized by engineering and operational teams.

• AI Agent Engineering: Build, train, and integrate autonomous AI agents and LLM-

driven workflows capable of interpreting data, executing commands, and safely

controlling desktop and web-based software.

• Workflow Automation: Translate complex workflows into intuitive software features

and autonomous agent actions, minimizing manual data entry and operational

bottlenecks.

• Data Integration: Handle high-frequency data streams and integrate them seamlessly

into user interfaces and backend AI models.

• Architecture & Scalability: Ensure high performance, security, and scalability across

cloud infrastructure (AWS/Azure), local desktop environments, and potential edge

computing setups.

• Cross-Functional Collaboration: Work closely with domain experts and end-users to

translate field challenges into technical product requirements.

Required Qualifications & Experience

• Experience: Minimum of 5 years of professional software development experience,

with a proven track record of delivering production-ready web and desktop

applications.

• Programming Languages: Strong proficiency in Python, JavaScript/TypeScript, and at

least one compiled language (C#, C++, or Java).• Web & Desktop Frameworks: Hands-on experience with modern frontend

frameworks (React, Angular, or Vue.js), Node.js, and desktop application development

(Electron, WPF, Qt, or Tauri).

• AI & Agent Tooling: Demonstrated experience building AI agents using LLM APIs

(OpenAI, Anthropic), open-source models (Hugging Face), LangChain, LlamaIndex,

AutoGPT, or custom agent architectures.

• Automation & UI Control: Expertise in software control mechanisms using tools like

Selenium, Playwright, PyAutoGUI, Appium, or computer vision-based GUI automation to

allow AI agents to navigate software.

• Cloud, DevOps & Databases: Experience with Git, Docker, CI/CD pipelines, cloud

platforms (AWS/Azure/GCP), RESTful APIs, GraphQL, and relational/NoSQL databases.

Preferred Qualifications (Strong Plus)

• Industry Domain Expertise: Prior hands-on development experience within the oil and

gas sector, specifically focused on drilling, completions, rig operations, or subsurface

engineering software.

• Data & Protocols: Familiarity with oilfield data standards (e.g., WITSML, OPC-UA) and

time-series databases.

• Experience deploying AI models and agents in edge or low-connectivity environments

(such as offshore rigs or remote drilling sites).

• Familiarity with safety-critical software design and cybersecurity standards in

industrial control systems (ICS/SCADA).

• Degree in Computer Science, Software Engineering, Petroleum Engineering, or a related technical discipline.

Position Overview

We are seeking a versatile Senior Full Stack & AI Agent Developer to architect, build, and

maintain end-to-end software solutions spanning web platforms, desktop applications, and

autonomous AI agents capable of interacting with and controlling these software systems.

The ideal candidate will bridge traditional engineering software with cutting-edge artificial

intelligence to automate data processing and enhance operational decision-making. While

not strictly required, a background or strong interest in the energy sector—specifically

drilling and completion operations—is highly desirable.

Key Responsibilities

• Full Stack Development: Design, develop, and deploy robust web applications and

native desktop software utilized by engineering and operational teams.

• AI Agent Engineering: Build, train, and integrate autonomous AI agents and LLM-

driven workflows capable of interpreting data, executing commands, and safely

controlling desktop and web-based software.

• Workflow Automation: Translate complex workflows into intuitive software features

and autonomous agent actions, minimizing manual data entry and operational

bottlenecks.

• Data Integration: Handle high-frequency data streams and integrate them seamlessly

into user interfaces and backend AI models.

• Architecture & Scalability: Ensure high performance, security, and scalability across

cloud infrastructure (AWS/Azure), local desktop environments, and potential edge

computing setups.

• Cross-Functional Collaboration: Work closely with domain experts and end-users to

translate field challenges into technical product requirements.

Required Qualifications & Experience

• Experience: Minimum of 5 years of professional software development experience,

with a proven track record of delivering production-ready web and desktop

applications.

• Programming Languages: Strong proficiency in Python, JavaScript/TypeScript, and at

least one compiled language (C#, C++, or Java).• Web & Desktop Frameworks: Hands-on experience with modern frontend

frameworks (React, Angular, or Vue.js), Node.js, and desktop application development

(Electron, WPF, Qt, or Tauri).

• AI & Agent Tooling: Demonstrated experience building AI agents using LLM APIs

(OpenAI, Anthropic), open-source models (Hugging Face), LangChain, LlamaIndex,

AutoGPT, or custom agent architectures.

• Automation & UI Control: Expertise in software control mechanisms using tools like

Selenium, Playwright, PyAutoGUI, Appium, or computer vision-based GUI automation to

allow AI agents to navigate software.

• Cloud, DevOps & Databases: Experience with Git, Docker, CI/CD pipelines, cloud

platforms (AWS/Azure/GCP), RESTful APIs, GraphQL, and relational/NoSQL databases.

Preferred Qualifications (Strong Plus)

• Industry Domain Expertise: Prior hands-on development experience within the oil and

gas sector, specifically focused on drilling, completions, rig operations, or subsurface

engineering software.

• Data & Protocols: Familiarity with oilfield data standards (e.g., WITSML, OPC-UA) and

time-series databases.

• Experience deploying AI models and agents in edge or low-connectivity environments

(such as offshore rigs or remote drilling sites).

• Familiarity with safety-critical software design and cybersecurity standards in

industrial control systems (ICS/SCADA).

• Degree in Computer Science, Software Engineering, Petroleum Engineering, or a related technical discipline.

Senior Software Architect

Posted by Sandeep C

Bengaluru (Bangalore)

8 - 16 yrs

₹1L - ₹2L / yr (ESOP available)

Large Language Models (LLM)

Agentic AI

Applied mathematics

Key Responsibilities:

· Architectural Leadership: Design and lead the development of robust, scalable AI architectures, ensuring high performance, reliability, and security.

· Applied Mathematics & Statistics: Apply statistical analysis, numerical computation, and mathematical modeling to derive insights from large-scale data and optimize model performance.

· Deep Learning Development: Design, train, and deploy advanced Deep Learning (DL) models.

· Technical Mentorship: Mentor engineering teams on best practices for AI/ML, coding standards, and architectural design.

· Model Optimization: Optimize models for speed, efficiency, and accuracy using techniques like pruning, quantization, or GPU acceleration.

· Strategy & Innovation: Evaluate and select appropriate AI frameworks, tools, and platforms, staying abreast of cutting-edge research and industry trends.

Qualifications:

Required:

· Education: Master's or PhD in Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field.

· Experience: 10+ years of experience in software development, with at least 3-5 years in a Applied Mathematics and Deep learning.

· AI/ML Expertise: Proven experience designing and deploying deep learning models in production using frameworks.

· Mathematics/Statistics: Strong proficiency in linear algebra, calculus, probability, and statistical methods.

· Programming Skills: Expert-level coding skills in Python (NumPy, Pandas, Scikit-learn) and experience with languages like Java or C++.

Key Competencies:

Strategic mindset with deep operational awareness.
Excellent communication and stakeholder management skills.
Ability to simplify complex technical concepts for executive reporting.
Strong leadership, people development, and cross-functional influencing skills.

Bias for action and a relentless focus on continuous improvement.

Key Responsibilities:

· Architectural Leadership: Design and lead the development of robust, scalable AI architectures, ensuring high performance, reliability, and security.

· Applied Mathematics & Statistics: Apply statistical analysis, numerical computation, and mathematical modeling to derive insights from large-scale data and optimize model performance.

· Deep Learning Development: Design, train, and deploy advanced Deep Learning (DL) models.

· Technical Mentorship: Mentor engineering teams on best practices for AI/ML, coding standards, and architectural design.

· Model Optimization: Optimize models for speed, efficiency, and accuracy using techniques like pruning, quantization, or GPU acceleration.

· Strategy & Innovation: Evaluate and select appropriate AI frameworks, tools, and platforms, staying abreast of cutting-edge research and industry trends.

Qualifications:

Required:

· Education: Master's or PhD in Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field.

· Experience: 10+ years of experience in software development, with at least 3-5 years in a Applied Mathematics and Deep learning.

· AI/ML Expertise: Proven experience designing and deploying deep learning models in production using frameworks.

· Mathematics/Statistics: Strong proficiency in linear algebra, calculus, probability, and statistical methods.

· Programming Skills: Expert-level coding skills in Python (NumPy, Pandas, Scikit-learn) and experience with languages like Java or C++.

Key Competencies:

Strategic mindset with deep operational awareness.
Excellent communication and stakeholder management skills.
Ability to simplify complex technical concepts for executive reporting.
Strong leadership, people development, and cross-functional influencing skills.

Bias for action and a relentless focus on continuous improvement.

AI GEO Engineer

Posted by Pramila Ranjane

Pune

1 - 4 yrs

₹12L - ₹25L / yr

GEO

AEO

SGE initiatives

Generative AI

Fine-tuning LLMs

+5 more

🔹 Key Responsibilities

• Design, develop, and deploy production-grade AI/ML and Generative AI solutions

• Work on GEO, AEO, and SGE initiatives to improve visibility across AI-driven search platforms

• Optimize content and digital experiences for conversational queries and LLM-based search

• Develop solutions using LLMs, NLP, embeddings, semantic search, RAG, and vector databases

• Analyze search intent, AI-generated responses, citations, retrieval patterns, and content discoverability

• Build frameworks to measure GEO/AEO strategies and AI-search performance

• Collaborate with Product, Engineering, Content, SEO, Marketing, and Business teams

• Improve solution accuracy, relevance, latency, and user experience

🔹 Mandatory Requirements

✅ 1–4 years of professional experience

✅ Minimum 1 year of hands-on experience in GEO, AEO, or SGE

✅ Experience with prompt engineering, embeddings, vector search, or RAG systems

✅ Understanding of semantic search and entity-based optimization

✅ Exposure to ChatGPT, Google Gemini, or similar LLM platforms

✅ Knowledge of schema, context building, content structuring, and knowledge representation

🎓 Preferred Education

B.Tech, M.Tech, Integrated M.Sc., or MS from a Tier-1 engineering institute such as IIT, NIT, BITS, VIT, DTU, or NSUT.