AI Engineer at Meraki Labs · Bengaluru (Bangalore) · 3 - 4 years · ₹30L - ₹50L / yr · Raised funding · Posted 12 Dec 2025
Python
FlaskJob Title: AI Engineer
Location: Bangalore, India
About Us:
Meraki Labs stands at the forefront of India's deep-tech innovation landscape, operating as a dynamic venture studio established by the visionary entrepreneur Mukesh Bansal. Our core mission revolves around the creation and rapid scaling of AI-first and truly "moonshot" startups, nurturing them from their nascent stages into industry leaders. We achieve this through an intensive, hands-on partnership model, working side-by-side with exceptional founders who possess both groundbreaking ideas and the drive to execute them.
Currently, Meraki Labs is channeling its significant expertise and resources into a particularly ambitious endeavor: a groundbreaking EdTech platform. This initiative is poised to revolutionize the field of education by democratizing access to world-class STEM learning for students globally. Our immediate focus is on fundamentally redefining how physics is taught and experienced, moving beyond traditional methodologies to deliver an immersive, intuitive, and highly effective learning journey that transcends geographical and socioeconomic barriers. Through this platform, we aim to inspire a new generation of scientists, engineers, and innovators, ensuring that cutting-edge educational resources are within reach of every aspiring learner, everywhere.
Role Overview:
As an AI Engineer on the Capacity team, you will design, build, and deploy the intelligent systems that power our AI Tutor and Simulation Lab.
You’ll collaborate closely with prompt engineers, product managers, and full-stack developers to build scalable AI features that connect language, reasoning, and real-world learning. This is not a traditional ML ops role, it’s an opportunity to engineer how intelligence flows across the product: from tutoring interactions to real-time physics reasoning.
Your Core Impact
- Build the AI backbone that drives real-time tutoring, contextual reasoning, and simulation feedback.
- Translate learning logic and educational goals into deployable, scalable AI systems.
- Enable the AI Tutor to think, reason, and respond based on structured academic material and live learner inputs.
Key Responsibilities:
1. AI System Architecture & Development
- Design and develop scalable AI systems that enable chat-based tutoring, concept explainability, and interactive problem solving.
- Implement and maintain model-serving APIs, vector databases, and context pipelines to connect content, learners, and the tutor interface.
- Contribute to the design of the AI reasoning layer that interprets simulation outputs and translates them into learner-friendly explanations.
2. Simulation Lab Intelligence
- Work with the ML team to integrate LLMs with the Simulation Lab; enabling the system to read experiment variables, predict outcomes, and explain results dynamically.
- Create evaluation loops that compare student actions against expected results and generate personalized feedback through the tutor.
- Support the underlying ML logic for physics-based prediction and real-time data flow between lab modules and the tutor layer.
3. Model Integration & Optimization
- Fine-tune, evaluate, and deploy LLMs or smaller domain models that serve specific platform functions.
- Design retrieval and grounding workflows so that all model outputs reference the correct textbook or course material.
- Optimize performance, latency, and scalability for high-traffic, interactive learning environments.
4. Collaboration & Research
- Partner with Prompt Engineers to ensure reasoning consistency across tutoring and simulations.
- Work with Product and Education teams to define use cases that align AI behavior with learning goals.
- Stay updated with new model capabilities and research advancements in RAG, tool use, and multi-modal learning systems.
5. Data & Infrastructure
- Maintain robust data pipelines for model inputs (textbooks, transcripts, lab data) and evaluation sets.
- Ensure privacy-safe data handling and continuous model performance tracking.
- Deploy and monitor AI workloads using cloud platforms (AWS, GCP, or Azure).etc.
Soft Skills:
- Strong problem-solving and analytical abilities.
- Eagerness to learn, innovate and deliver impactful results.
Required Qualifications & Skills
- 3–4 years of experience in AI engineering, ML integration, or backend systems for AI-driven products.
- Strong proficiency in Python, with experience in frameworks like FastAPI, Flask, or LangChain.
- Familiarity with LLMs, embeddings, RAG systems, and vector databases (Pinecone, FAISS, Chroma, etc.).
- Experience building APIs and integrating with frontend components.
- Working knowledge of cloud platforms (AWS, GCP, Azure) and model deployment environments.
- Understanding of data structures, algorithms, and OOP principles.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Flask
DjangoJob Title: Senior AI Engineer
Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred
Employment Type: Full-time
Department: Artificial Intelligence / Engineering
Location: Bangalore
We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.
The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.
Key Responsibilities
1. AI Solution Design and Development
- Design, develop, and deploy end-to-end AI and machine learning solutions.
- Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
- Develop scalable AI architectures and reusable components for enterprise applications.
- Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.
2. Generative AI and LLM Engineering
- Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
- Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
- Build AI agents and multi-step workflows using appropriate orchestration frameworks.
- Implement prompt engineering, structured outputs, tool calling, and context management.
- Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.
3. Machine Learning and Data Engineering
- Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
- Work with structured, unstructured, and multimodal data.
- Build data preprocessing, feature engineering, model training, and validation pipelines.
- Apply appropriate model evaluation techniques and statistical methods.
4. Production Deployment and MLOps / LLMOps
- Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
- Build automated CI/CD pipelines for AI applications and model deployment.
- Implement model versioning, experiment tracking, monitoring, and performance evaluation.
- Optimize inference latency, throughput, infrastructure utilization, and operating costs.
- Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.
5. Software Engineering and System Architecture
- Develop production-quality applications and APIs using Python and modern backend frameworks.
- Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
- Integrate AI services with enterprise applications, databases, and third-party systems.
- Use relational databases, vector databases, caching, and distributed systems effectively.
- Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.
6. Security, Quality, and Responsible AI
- Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
- Apply appropriate privacy, security, and compliance controls to AI systems.
- Evaluate hallucinations, bias, reliability, and failure modes.
- Design human-in-the-loop workflows and fallback mechanisms for critical use cases.
7. Technical Leadership and Collaboration
- Lead technical design discussions and mentor junior AI engineers.
- Define engineering standards, coding practices, and AI development guidelines.
- Collaborate with cross-functional teams to deliver measurable business outcomes.
- Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
- Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.
Required Technical Skills
Programming and Backend Development
- Strong proficiency in Python.
- Experience with FastAPI, Flask, or Django.
- Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
- Experience with Git, automated testing, and CI/CD.
AI and Machine Learning
- Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
- Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
- Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.
Generative AI and LLMs
- Hands-on experience building production applications using LLM APIs or open-source models.
- Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
- Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
- Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
- Experience with tool calling, structured generation, agent workflows, and LLM evaluation.
Cloud and Infrastructure
- Experience with AWS, Azure, or Google Cloud.
- Working knowledge of Docker and Kubernetes.
- Familiarity with model serving, scalable APIs, monitoring, and production deployments.
- Experience with MLOps or LLMOps practices.
Databases and Data Processing
- Strong SQL skills and experience with relational databases.
- Familiarity with NoSQL databases, data pipelines, and distributed processing.
- Experience working with unstructured data, document ingestion, and retrieval systems.
Preferred Qualifications
- Experience with fine-tuning, quantization, or serving open-source LLMs.
- Experience with multimodal AI, including text, image, audio, or video.
- Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
- Experience with AI security, guardrails, governance, and responsible AI.
- Experience optimizing AI workloads for latency, scalability, and cost.
- Contributions to open-source AI projects, research, or technical publications.
Education
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
- Equivalent practical experience and demonstrated engineering expertise may be considered.
Key Success Metrics
- Quality, accuracy, and reliability of deployed AI solutions.
- Production adoption and measurable business impact.
- Reduction in inference costs and response latency.
- RAG retrieval quality and reduction in unsupported or incorrect responses.
- System availability, scalability, and operational stability.
- Delivery timelines, code quality, and technical mentoring effectiveness.
Strong Senior AI Engineer Profile with production-grade LLM and Python expertise
2
Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments
3
Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design
4
Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)
5
Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.
6
Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale
7
Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)
8
Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)
9
Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).
10
Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges
11
Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation
12
Mandatory (Company): B2B Tech/Consulting/B2B Software
PythonProduct Engineer — Role Summary
We are looking for a Product Engineer to build user-facing products that combine AI capabilities with practical applications. You will work at the intersection of software engineering and AI, developing autonomous, agent-driven systems that solve complex educational and research problems.
Key Responsibilities:
- Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
- Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
- Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
- Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
- AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
- User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
- Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
- Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.
The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.
About LeadSquared
LeadSquared is a leading sales execution and marketing automation platform trusted by 2,000+ businesses globally, including healthcare, education, financial services, and real estate. Headquartered in Bengaluru with offices across the US, UK, UAE, and Southeast Asia, we empower sales teams to close faster, smarter, and at scale.
Our AI team is at the forefront of integrating cutting-edge large language model capabilities into enterprise workflows — building intelligent agents, copilots, and automation systems that redefine how businesses operate.
Role Overview
We are looking for a Senior AI Engineer with hands-on experience building LLM-powered agents and agentic AI systems. You will design, develop, and deploy autonomous AI pipelines that solve complex, multi-step business problems — from lead qualification and follow-up automation to intelligent CRM workflows and beyond.
This role is ideal for someone who is deeply excited about the frontier of AI, can move fast, and wants their work to directly impact millions of sales professionals worldwide.
Key Responsibilities
•
Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
•
Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.
•
Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.
•
Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.
•
Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.
•
Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.
•
Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.
•
Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.
•
Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.
Required Qualifications
Experience
•
2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.
•
Proven experience shipping LLM-based products or agentic AI systems into production environments.
Technical Skills
•
Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.
•
Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).
•
Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.
•
Solid understanding of RAG architectures, embedding models, and semantic search.
•
Experience with vector databases and similarity search infrastructure.
•
Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).
Problem-Solving & Mindset
•
Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.
•
Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.
•
Ability to balance research exploration with engineering pragmatism to ship reliable systems.
Preferred Qualifications
•
Experience with multi-agent orchestration and agent memory systems (short-term and long-term).
•
Familiarity with fine-tuning or RLHF workflows for domain adaptation.
•
Background in NLP, information retrieval, or conversational AI.
•
Prior experience in B2B SaaS or CRM domain is a plus.
•
Contributions to open-source AI/ML projects or published research/blogs.
•
Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services
Machine Learning (ML)PythonThis is a remote position.
About Leegality:
Leegality works with large Indian businesses to digitally transform critical compliance processes in a fast, easy and secure way.
We have multiple products across 2 categories:
Document Infrastructure:
Products that help businesses build paperless processes at scale:
- Document Execution Workflow: A unified platform for businesses to digitally execute (eSign, eStamp, Template Pre-fill, Document Fraud Prevention etc.) agreements, forms and other documents in a compliant way. Currently in use by 2000+ Indian businesses from giants like HDFC and SBI Cards to high-growth disruptors like goDigit and Cars24.
- Contract Management: An AI-powered platform for businesses to quickly review, negotiate and take action on contract
- Signstation: A simple platform for businesses to digitally sign simple documents like invoices, policies and letters in a cost effective manner
Consent Infrastructure:
- Consentin: An end-to-end DPDP and Privacy compliance platform for Indian businesses
- Consentin Lens: A data discovery platform for businesses to identify the personal data they collect and store.
If you’re interested in building mission critical software that operates at population scale (75 million + Indians have signed at least one document through Leegality) then join Leegality.
Curious about our impact? Explore our customer success stories: leegality.com/case-studies
Our Culture
At Leegality, trust, ownership, transparency, and having fun while doing meaningful work are core to how we operate — not just values on paper. Our team rated us an incredible 97 eNPS for FY 2023–24 — the highest among 175+ startups surveyed.
We focus deeply on helping our people grow and stay motivated. Some of the perks you’ll enjoy:
- Flexible working hours
- Hybrid work setup
- Bi-annual performance appraisals
- A culture that rewards initiative, curiosity, and impact
If you're looking for a place where you can make a real difference while working with smart, driven, and genuinely nice people, welcome to Leegality.
Location: Hybrid
Job Brief:
- As a Machine Learning Engineer specializing in Computer Vision (CV) and Natural Language Processing (NLP), you will develop solutions to interesting technical problems, exploring exciting growth opportunities and having a real impact on our product, particularly focusing on document and content intelligence.
- To ensure success, you should demonstrate solid data science knowledge and experience in a related ML, CV, or NLP role. A first-class engineer will be someone whose expertise enhances our systems for document intelligence and content processing
Responsibilities:
- Designing machine learning systems, self-running artificial intelligence (AI) software, and specialized models for Computer Vision and Natural Language Processing applications.
- Transforming data science prototypes and applying appropriate deep learning algorithms and tools to text and image/document data.
- Solving complex CV and NLP problems with multi-layered data types, such as image/document classification, information extraction, semantic search, and object detection.
- Optimizing existing machine learning models, with a focus on high-performance model deployment for CV and NLP tasks.
- Developing ML algorithms (including large language models/LLMs and computer vision models) to analyze huge volumes of historical text, image, and document data to make predictions and automate workflows.
- Running tests, performing statistical analysis, and interpreting test results for CV/NLP model performance.
- Documenting machine learning processes, model architectures, and data pipelines.
- Keeping abreast of developments in machine learning, Computer Vision, and Natural Language Processing.
Requirements:
- 3+ years of relevant experience in Machine Learning Engineering, with a strong focus on Computer Vision and/or Natural Language Processing.
- Advanced proficiency with Python.
- Extensive knowledge of ML frameworks, libraries (e.g., PyTorch, Transformers), data structures, data modeling, and software architecture.
- Experience with building and maintaining scalable RESTful APIs (e.g., FastAPI).
- In-depth knowledge of mathematics, statistics, deep learning (CNNs, RNNs, Transformers), and algorithms.
- Superb analytical and problem-solving abilities, especially for unstructured data challenges.
- Great communication and collaboration skills.
- Excellent time management and organizational abilities.
- Experience with cloud platforms (e.g., AWS) for model deployment and MLOps.
Recruitment Process:
- Our hiring process combines AI-powered evaluations with structured interviews to ensure a fair and seamless experience.
- You will be contacted via email with the next steps upon being shortlisted.
- The process may include Assessments, AI-enabled interviews, and In-Person Interviews with our team.
- Final selection and CTC will be based on your overall performance and experience.
Apply directly through our career page: https://careers.leegality.com/jobs/Careers
For more information about us please visit our:
Our Company and Culture: https://bit.ly/3Iqm5SB
Our Website: www.leegality.com/
Our LinkedIn Page: www.linkedin.com/company/leegality/
Leegality's Privacy Notice: https://www.leegality.com/employee-privacy-notice
PythonDear Candidate,
Thanks for showing interest towards the opportunity!!!
As discussed, we have an immediate opening for Python + Agentic AI Developer with Mphasis for Bangalore location.
Experience: 4 Years
Mandatory Skills: Python, Agentic AI, Generative AI (GenAI), LLMs, AI Agents.
Kindly share your updated resume and availability for the opportunity.
Hiring for AI Engineer
Exp: 5 - 8 yrs
Edu : BE/B.Tech/MCA
Work Location : Pune
Skill Set:
Total experience ranging from 5 - 8 years in software engineering/AI roles
Min 5 years strong programming experience in Python or Typescript is a MUST
Min 2.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks
Experience with cloud platforms (AWS/Azure/GCP)
Machine Learning (ML)PythonJob Overview
We are looking for an experienced AI/ML Engineer with strong full-stack development skills to design, develop, and deploy AI-powered applications. The ideal candidate should be proficient in Python backend development, frontend technologies, and LLM integration, with the ability to build end-to-end AI-native products, custom dashboards, and lightweight applications.
This role focuses on applying AI to real-world business problems through rapid prototyping, prompt engineering, Retrieval-Augmented Generation (RAG), and scalable application development.
Key Responsibilities
- Design, develop, and deploy end-to-end AI-powered applications.
- Build backend services and REST APIs using Python.
- Develop responsive frontends using React, Next.js, or similar frameworks.
- Integrate LLM APIs such as OpenAI, Claude, and Hugging Face into production applications.
- Build custom dashboards, internal tools, micro-apps, and AI-driven business solutions.
- Implement prompt engineering, embeddings, RAG pipelines, and context-aware AI systems.
- Rapidly prototype and validate ideas using modern AI-assisted development workflows.
- Optimize application performance, scalability, reliability, and AI usage costs.
- Work with data processing libraries such as Pandas and NumPy.
- Collaborate with product and business teams to translate requirements into functional AI solutions.
- Take ownership of features from ideation and development through testing, deployment, and production support.
Required Skills & Qualifications
- 2+ years of experience in software engineering, AI engineering, or application development.
- Strong proficiency in Python and backend development.
- Hands-on experience with React, Next.js, or similar frontend technologies.
- Practical experience integrating LLM APIs such as OpenAI, Claude, or Hugging Face.
- Understanding of REST APIs, microservices, and system design principles.
- Knowledge of prompt engineering, embeddings, and RAG architectures.
- Experience with data processing using Pandas and NumPy.
- Ability to independently build, test, and deploy full-stack applications.
- Strong problem-solving, debugging, and analytical skills.
- Ability to work in a fast-paced environment with a strong ownership mindset.
Good to Have
- Experience with LangChain, LlamaIndex, or similar AI frameworks.
- Familiarity with vector databases such as Pinecone, Weaviate, or Supabase.
- Experience building AI chat interfaces, copilots, assistants, and custom dashboards.
- Familiarity with cloud platforms, application deployment, and production monitoring.
- Experience with AI-assisted coding tools and rapid prototyping workflows.
What We Are Looking For
- A full-stack AI engineer who is comfortable working across both backend and frontend development.
- Someone focused on building practical AI-powered products rather than only training machine learning models.
- A developer who can prototype quickly, validate ideas, and iterate efficiently using a “vibe coding” approach.
- A self-driven professional who can take ownership of a feature from concept to production.
Please note that this job is only for Ahmedabad location.
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
🔹 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.
PythonLocation -Bangalore
Work Mode -Fully in-office, 6 days a week
Experience -3+ years in AI engineering, with LLM-based systems shipped to production
About the Role
● The AI Engineer will own systems that read, search and reason over project and tender documents.
● Inputs include large tender packages, scanned PDFs, BOQs, tables, forms and engineering drawings such as P&IDs.; Outputs should include accurate answers with source references, structured data and workflows used by estimators and project teams in live bids, planning, procurement and execution.
● The role covers taking features from initial experiments to production and owning their accuracy, latency and cost after release.
Key Responsibilities
Agents and Retrieval
● Build AI agents that plan and execute multi-step tasks using document search, tools and APIs.
● Build RAG pipelines covering ingestion, retrieval, reranking and answers with source references.
● Use agent workflows within retrieval to refine searches, collect supporting evidence and verify outputs.
Document Intelligence
● Build extraction pipelines for PDFs, scanned documents, tables and forms.
● Convert unstructured tender content into structured data that the platform can use.
Vision and Image Understanding
● Build image-understanding workflows for P&IDs;, including symbols, equipment, labels and connections.
● Locate and remove selected elements from diagram images while preserving surrounding content.
Quality and Reliability
● Create evaluation datasets and automated checks to measure accuracy and catch regressions.
● Debug failures across retrieval, agent decisions, tool calls and model outputs.
Product and Production
● Build backend services and REST APIs. ● Integrate AI features into the Workwise web application.
● Take features from experiment to production while managing reliability, latency and cost
Required Qualifications
● 3+ years of hands-on experience building AI systems, including at least one LLM-based application running in production.
● Strong Python and software engineering fundamentals.
● Experience with backend services, REST APIs, databases, authentication and frontend integration.
● Experience building applications using LLM APIs.
● Working knowledge of RAG, embeddings, retrieval and vector databases.
● Experience with tool calling, structured outputs and multi-step agent workflows.
● Working understanding of multimodal models and image processing for visual analysis and editing.
● Systematic evaluation skills: measuring results and investigating system failures.
● Ownership of implementation, testing, deployment and ongoing improvement.
Good to Have
● OCR, document layout analysis or complex table extraction.
● Computer vision experience, including object detection, segmentation or image inpainting.
● Familiarity with construction, procurement or tender documents.
● Cloud infrastructure and production monitoring.
● Working knowledge of Go.
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon