Senior Field AI Engineer

Role Overview 

We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale. 

Key Responsibilities 

Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production. 

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

Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production. 

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. 

Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability. 

Strong Lead AI Engineer Profile with agentic systems architecture expertise

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Mandatory (Experience 1): Must have 6+ years of software or ML engineering, with at least recent 2+ years building and shipping production LLM or agentic AI systems

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Mandatory (Tech skill 1): Must have hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI), with experience debugging framework internals

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Mandatory (Tech skill 2): Must have direct SDK experience with Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder

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Mandatory (Tech skill 3): Must have Python mastery — production-quality code, type annotations, unit and integration tests, packaging, and performance profiling

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Mandatory (Tech skill 4): Must have RAG pipeline depth — embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses

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Mandatory (Tech skill 5): Must have cloud deployment experience with AWS, Azure, or GCP; Docker, Kubernetes, IaC basics (Terraform or CDK), and CI/CD pipelines

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Mandatory (Tech skill 6): Must have agent observability experience — LangSmith, Helicone, or equivalent — with ability to diagnose latency, cost, and quality issues in production traces

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Mandatory (Tech skill 8) : Must have owned full sub-system designs — agent topology, data flows, API contracts, failure handling — for a bounded scope, and built multi-agent graphs, tool orchestration, retrieval, and memory systems.

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Mandatory (Tech skill 7): Must have track record of shipping 2+ agentic or ML systems to production — not just proof-of-concepts — with documented performance benchmarks

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Mandatory (Communication): Must be able to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders

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Mandatory (Note 1) : Role is Hybrid, WFH flexibility as well upto 6 days a month

Applied AI Engineer

About the Role

We are building an AI-powered platform that transforms credit origination by automating manual processes and enabling lenders to underwrite more loans with greater accuracy and lower risk.

We are looking for an experienced Applied AI Engineer to design and build production-grade Agentic AI systems capable of complex, multi-step reasoning across financial data, legal documents, and other unstructured information.

As an early engineering team member, you will take ownership of core AI architecture, multi-agent orchestration, reasoning workflows, evaluation frameworks, and scalable AI infrastructure.

Key Responsibilities

• Design and develop stateful, multi-agent AI systems for complex reasoning and decision-making.
• Build and optimize LLM pipelines using frameworks such as LangChain, DSPy, and GEPA.
• Implement RAG, prompt optimization, memory management, tool integration, and human-in-the-loop workflows.
• Develop AI evaluation frameworks using LLM-as-a-Judge and scenario-based testing.
• Implement AI observability and monitoring to track accuracy, latency, cost, and system performance.
• Build ETL pipelines and work with structured and unstructured data, SQL/NoSQL databases, vector databases, and graph-based data models.
• Integrate external tools and systems using modern agent protocols such as Model Context Protocol (MCP).
• Deploy and maintain AI services on AWS or Azure.
• Implement CI/CD pipelines, containerization, and Infrastructure as Code for reliable production deployments.

Required Qualifications

• 5+ years of professional software development experience in Python or JavaScript.
• Proven experience building and deploying production-level Agentic AI systems or complex LLM reasoning applications.
• Hands-on experience with LangChain and LLMOps, including tools such as Langfuse, Weights & Biases (W&B), or Helicone.
• Strong understanding of LLMs, RAG, prompt engineering, multi-agent orchestration, memory management, and tool calling.
• Experience with DSPy, GEPA, or advanced LLM inference and optimization techniques.
• Strong data engineering fundamentals, including ETL, SQL/NoSQL databases, and vector databases.
• Familiarity with MCP and human-in-the-loop workflows.
• Experience with AWS or Azure and modern DevOps practices, including Docker, CI/CD, and Infrastructure as Code.

Good to Have

• Experience with RLHF or RLAIF.
• Knowledge of model fine-tuning techniques such as LoRA and QLoRA.
• Experience with graph databases and graph-based data models.
• Exposure to financial services, credit underwriting, risk analysis, or document-intensive AI applications.

Why Join Us?

• Work on advanced Agentic AI and complex reasoning systems beyond conventional chatbot applications.
• Build foundational AI architecture and production-grade LLM workflows.
• Take ownership of technical decisions and system design as an early engineering team member.
• Solve challenging problems at the intersection of AI, finance, and intelligent automation.

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

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Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.

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Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.

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Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.

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Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.

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Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.

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Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.

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Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.

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Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.

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Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.

Required Qualifications

Experience

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2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.

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Proven experience shipping LLM-based products or agentic AI systems into production environments.

Technical Skills

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Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.

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Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).

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Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.

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Solid understanding of RAG architectures, embedding models, and semantic search.

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Experience with vector databases and similarity search infrastructure.

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Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).

Problem-Solving & Mindset

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Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.

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Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.

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Ability to balance research exploration with engineering pragmatism to ship reliable systems.

Preferred Qualifications

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Experience with multi-agent orchestration and agent memory systems (short-term and long-term).

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Familiarity with fine-tuning or RLHF workflows for domain adaptation.

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Background in NLP, information retrieval, or conversational AI.

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Prior experience in B2B SaaS or CRM domain is a plus.

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Contributions to open-source AI/ML projects or published research/blogs.

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Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services

Job 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.

Role Overview

We are looking for an experienced Data Scientist – Agentic AI with strong expertise in Python, Machine Learning, Generative AI, Large Language Models (LLMs), RAG and Agentic AI.

The ideal candidate should have hands-on experience in developing, fine-tuning, evaluating and deploying machine learning and GenAI solutions. The candidate should be comfortable working with open-source LLMs, LangChain/LangGraph, PySpark and AI observability/tracing frameworks.

The role involves building intelligent AI systems that can reason, use tools, retrieve information and execute multi-step tasks using Agentic AI architectures.

Mandatory Technical Skills

1. Data Science & Python

• 5+ years of experience in Data Science / Machine Learning / AI.
• Strong programming experience in Python.
• Strong understanding of data analysis, feature engineering and statistical techniques.
• Experience with Python ML and data science libraries such as:
• NumPy
• Pandas
• Scikit-learn
• Matplotlib / Seaborn
• Good understanding of data preprocessing, exploratory data analysis and experimentation.

2. Machine Learning & Statistics

• Strong understanding of Machine Learning fundamentals.
• Experience with supervised and unsupervised learning techniques.
• Knowledge of:
• Regression
• Classification
• Clustering
• Feature Engineering
• Model Selection
• Hyperparameter Tuning
• Cross-validation
• Strong understanding of Statistics / ML fundamentals.
• Ability to interpret model performance and statistical results.

3. Generative AI / LLM

• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Understanding of Transformer architecture and modern LLM-based applications.
• Experience working with commercial or open-source LLMs.
• Strong understanding of:
• Prompt Engineering
• Context Management
• Embeddings
• Tokenization
• LLM inference
• Hallucination mitigation

4. RAG – Retrieval Augmented Generation

• Strong hands-on experience developing RAG applications.
• Experience with:
• Document ingestion
• Chunking
• Embeddings
• Vector search
• Semantic search
• Retrieval pipelines
• Context retrieval
• Reranking
• Ability to optimize RAG pipelines for relevance, accuracy and latency.
• Experience integrating LLMs with enterprise knowledge sources.

5. Agentic AI

• Hands-on experience building Agentic AI / AI Agent solutions.
• Understanding of agent architecture and multi-step reasoning workflows.
• Experience with:
• AI Agents
• Multi-Agent systems
• Tool Calling
• Function Calling
• Agent orchestration
• Planning and reasoning workflows
• Memory
• Workflow automation
• Ability to build agents that can interact with tools, APIs, databases and external systems.

6. LangChain / LangGraph

• Strong hands-on experience with LangChain and/or LangGraph.
• Experience building LLM workflows and agent-based applications.
• Understanding of:
• Chains
• Agents
• Tools
• State management
• Graph-based workflows
• Agent orchestration
• Retrieval workflows
• Experience designing scalable Agentic AI workflows.

7. LLM Fine-Tuning

• Hands-on experience with LLM fine-tuning.
• Understanding of techniques such as:
• Supervised Fine-Tuning (SFT)
• Parameter-Efficient Fine-Tuning
• LoRA
• QLoRA
• Experience preparing datasets for fine-tuning.
• Ability to evaluate fine-tuned models against baseline models.
• Understanding of model optimization and inference considerations.

8. BERT / LLaMA / Open-Source LLMs

Experience working with one or more open-source / transformer-based models such as:

• BERT
• LLaMA / Llama
• Mistral
• Gemma
• Qwen
• Other open-source LLMs

Candidate should understand model loading, inference, fine-tuning and evaluation.

9. PySpark

• Strong experience with PySpark for large-scale data processing.
• Experience working with large datasets and distributed data processing.
• Knowledge of:
• Data transformations
• Data cleaning
• Aggregations
• Joins
• Spark SQL
• Performance optimization
• Ability to build scalable data processing pipelines.

10. Model Validation & Evaluation

• Experience validating and evaluating ML and GenAI models.
• Understanding of traditional ML evaluation metrics.
• Experience evaluating LLM/RAG applications using relevant quality metrics.
• Ability to compare model performance and identify areas for improvement.
• Experience with:
• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC
• Retrieval metrics
• LLM response quality
• Groundedness / relevance
• Experience designing evaluation datasets and test cases is preferred.

11. AI Tracing / Observability

• Experience with AI/LLM tracing and observability.
• Ability to monitor AI applications in production.
• Experience tracking:
• LLM requests/responses
• Latency
• Token usage
• Errors
• Retrieval performance
• Agent/tool execution
• Model performance
• Exposure to tools/frameworks such as LangSmith, OpenTelemetry, Arize Phoenix, MLflow or similar is preferred.

12. Model Deployment

• Experience deploying ML/LLM/GenAI solutions into production.
• Exposure to cloud and/or on-premise model deployment.
• Experience with model serving, APIs and production inference.
• Knowledge of deployment environments such as:
• AWS
• Azure
• GCP
• On-premise infrastructure
• Experience with Docker, APIs and CI/CD is an advantage.

Key Responsibilities

• Design, develop and deploy Data Science, Machine Learning and GenAI solutions.
• Build production-ready RAG and Agentic AI applications.
• Develop intelligent agents capable of tool calling, reasoning and multi-step task execution.
• Build LLM-powered applications using LangChain/LangGraph.
• Work with open-source LLMs including BERT, LLaMA and other transformer-based models.
• Fine-tune LLMs for specific business use cases.
• Develop scalable data processing pipelines using PySpark.
• Perform data analysis, feature engineering and statistical modeling.
• Develop and maintain model validation and evaluation frameworks.
• Evaluate ML and LLM models using appropriate performance and quality metrics.
• Implement AI tracing, monitoring and observability for production GenAI systems.
• Deploy models and AI applications in cloud or on-premise environments.
• Optimize model performance, response quality, latency and cost.
• Troubleshoot issues related to model inference, retrieval, agents and LLM workflows.
• Collaborate with Data Scientists, ML Engineers, Software Engineers and business stakeholders.
• Convert business requirements into scalable AI/ML solutions.

Good to Have

• Experience with Vector Databases such as:
• FAISS
• Pinecone
• Weaviate
• Milvus
• Chroma
• Azure AI Search
• Experience with MLflow or similar ML lifecycle tools.
• Experience with Docker/Kubernetes.
• Experience with REST APIs / FastAPI.
• Knowledge of cloud AI/ML services.
• Experience with MLOps / LLMOps.
• Experience with multi-agent frameworks other than LangChain/LangGraph.
• Experience working with enterprise GenAI applications.

Ideal Candidate Profile

The ideal candidate should be a Data Scientist / ML Engineer with strong GenAI and Agentic AI experience, rather than a pure Python developer.

A strong candidate would typically have:

Data Science + Python + ML + Statistics + GenAI/LLM + RAG + Agentic AI + LangChain/LangGraph + LLM Fine-Tuning + Open-Source LLMs + PySpark + Model Evaluation + AI Observability + Model Deployment.

Core Mandatory Skills

Data Science, Python, Machine Learning, Statistics/ML Fundamentals, GenAI/LLM, RAG, Agentic AI, LangChain/LangGraph, LLM Fine-Tuning, BERT/LLaMA/Open-Source LLMs, PySpark, Model Validation/Evaluation, AI Tracing/Observability, Cloud/On-Prem Model Deployment.

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.

Forward-deployed engineers (FDEs) are Mactores' services layer. You embed with the customer's team, own outcomes from discovery through the production cutover, and personally carry the delivery commitment.

The agent platform we deploy absorbs 60–70% of engagement work, discovery, assessment, design, and testing. You absorb the judgment: target architecture, refactoring trade-offs, model selection, cutover strategy, and the decisions an agent platform cannot make. The agent absorbs scale. You absorb judgment. 

This is not a staff-augmentation seat and not an advisory role. You ship.

What you will do?

• Deliver production agentic AI systems and AWS modernization engagements on committed dates across three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps.
• Build and productionize AI agents, orchestration, retrieval pipelines, evaluation harnesses, observability running against real customer data, not demo data.
• Convert existing products into agents: expose product functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Convert existing Business processes into agents: expose process functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Embed directly with customer engineering teams. Run architecture sessions, defend design decisions, and align stakeholders from VP Engineering to CTO.
• Make agent decisions traceable and defensible, validation runs in parallel with live workloads, and outputs hold up to internal audit and regulators (HIPAA, PCI-DSS, FSI-grade governance where the vertical demands it).
• Feed field experience back into the platform and practice: your deployment patterns, integration playbooks, and edge cases shape how we deliver.

What are we looking for?

• Excellent communication skills (English) — verbal and written. Non-negotiable. You will present architecture to customer CTOs, write documents that hold up in audit, and defend judgment calls in the room. If you can build but not explain, this role is not a fit.
• You have shipped production agentic AI systems on AWS. Not POCs, not notebooks — systems running in production for real users. This is the primary qualification. Be prepared to walk through what you shipped, the decisions you made, and what broke.
• Deep understanding of agentic architecture — you can design an agent system from first principles and explain why each component exists:
• Agent design patterns: single-agent vs. multi-agent systems, supervisor/orchestrator patterns, hierarchical agent topologies, planner–executor separation, and when each applies.
• Orchestration: building and operating orchestrator agents that decompose tasks, route work to specialist agents or tools, and manage state across multi-step workflows (LangGraph, Strands Agents, CrewAI, or equivalent).
• Memory: short-term/working memory (context management, conversation state) and long-term memory (episodic and semantic stores, vector- and graph-backed retrieval), and the production trade-offs of each.
• Reflection and self-correction: critique loops, self-evaluation, retry-with-feedback patterns, and evaluation harnesses that catch agent failures before customers do.
• Tool use and function calling: schema design, tool-selection reliability, error handling, and agent-to-agent composition.
• RAG and retrieval pipelines: chunking, embedding, hybrid retrieval, reranking, and grounding agent decisions in customer data.
• Strong AWS production experience: Amazon Bedrock and AWS AI services, plus core platform services (Lambda, API Gateway, DynamoDB, RDS/Aurora, Glue, EMR, Redshift, Kinesis, or similar depending on specialization).
• Solid software engineering fundamentals Python, TypeScript, CI/CD, infrastructure-as-code, testing-driven development discipline.
• Experience with data or application modernization (database migration, legacy refactoring, data platform builds) is a strong plus, since agents run against these workloads.
• Indicative experience: roughly 3–10 years in engineering roles, with agentic AI / GenAI as your current day job. We have demonstrated agent-native expertise over tenure — an engineer with 3–4 years of hands-on agentic AI work typically outperforms a 12-year generalist on this work.

You'll be preferred if you've:

• US English verbal and written fluency 
• Delivery experience in one or more of our verticals: Financial Services, Healthcare & Life Sciences, Internet & Software, Manufacturing, or Telco/Media/Entertainment/Gaming/Sports.
• Model tuning and fine-tuning: systematic prompt engineering and optimization; parameter-efficient fine-tuning (LoRA/QLoRA or similar); instruction tuning; working knowledge of RLHF/DPO; sound judgment on when to fine-tune vs. prompt vs. RAG; and evaluation of tuned models against baselines. Fine-tuning experience on Amazon Bedrock or SageMaker is a plus.
• Experience with compliance-sensitive AI systems (HIPAA, PCI-DSS, SOC 2, data residency).
• Knowledge graph, code-analysis (AST), or CDC/streaming experience (Debezium, Kafka/MSK).
• Solid software engineering fundamentals — Java, C++, Go Lang, .Net, Rust
• Prior customer-facing consulting or forward-deployed experience.
• AWS certifications (Solutions Architect Professional, Machine Learning Specialty, or Data Analytics).

Why This Role?

• You own outcomes, not tickets. FDEs carry the delivery commitment personally — architecture, judgment, and cutover are yours.
• You work agent-native from day one. Our delivery model would not function without agents. You build with the platform, not around it.
• You ship. Engagements measured in weeks to production, legacy retired, outcomes named. No archived pilots.
• You compound. Field delivery informs the Aedeon platform roadmap; the platform's growth expands what you can deliver. Few engineering roles sit in that loop.

Key Skills:

• Agentic AI / AI Agents

• Python or Java

• LLMs & Generative AI

• RAG & Vector Databases

• LangChain / LangGraph / AutoGen / CrewAI

• REST APIs & Microservices

• Prompt Engineering

• AI Workflow Automation

Roles & Responsibilities:

• Design and develop AI agents and agentic workflows

• Build scalable backend services using Python or Java

• Integrate LLMs, APIs, tools, and external systems into AI workflows

• Develop RAG-based solutions and intelligent automation

• Design multi-step AI workflows with tool/function calling

• Evaluate, monitor, and optimize AI agent performance

• Collaborate with engineering and product teams to deliver production-ready AI solutions

Dear 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.