Enterprise Integration Engineer, ServiceNow / Splunk (Freelance)

We are a UK-based ecommerce company looking for a hands-on AI Automation & Integrations Engineer based in India to work directly with the founder.

Our goal is to progressively build an AI-first ecommerce operation by using AI, automation, APIs and software integrations to reduce manual work, improve decision-making and identify opportunities to increase revenue and profitability.

This is not a traditional data science or prompt engineering role. We are looking for a builder who can understand real business problems and turn them into reliable working systems.

What you will work on

• Build AI-powered automations and internal tools
• Connect ecommerce platforms and business systems through APIs and webhooks
• Build AI agents and LLM-powered workflows
• Automate repetitive operational processes
• Work with Shopify and ecommerce data
• Build automated reporting, alerts and business intelligence
• Develop inventory forecasting and operational tools
• Integrate AI with customer service, marketing, fulfilment and other business systems
• Identify new areas where AI and automation can improve efficiency, revenue or profitability
• Maintain and improve existing automations

Required skills

• Strong Python experience
• Strong understanding of REST APIs and webhooks
• Experience building software integrations and automations
• Practical experience using LLM APIs such as Claude, OpenAI or Gemini
• Experience working with databases such as PostgreSQL
• Git/GitHub
• Strong problem-solving ability
• Good written and spoken English
• Ability to work independently and take ownership of projects

Highly desirable

• n8n
• Shopify Admin API
• GraphQL
• JavaScript/TypeScript
• AI agents
• LangGraph/LangChain
• MCP
• AWS/GCP
• Ecommerce experience
• Data extraction/scraping
• Customer service or marketing integrations

We value real projects and practical ability more than certificates.

Candidates should be comfortable showing examples of AI automations, integrations, agents or software systems they have personally built.

Location: Remote, India

Experience: 3-7 years

Salary: ₹15-22 LPA depending on experience

Job Title: Senior Fullstack Engineer – ServiceNow

Company: Cloud Sara Technologies Pvt. Ltd.

Experience: 8–12 Years

Location: Bangalore, Hyderabad, Chennai, Mumbai, Ahmedabad

Work Mode: Hybrid – 2 days work from office mandatory

Employment Type: Contract – 6 Months

Notice Period: Immediate Joiners Only

Shift: 1:00 PM – 11:00 PM IST

Interview Process: L1 in-person interview is mandatory

Job Description

We are looking for an experienced Senior Fullstack Engineer with strong hands-on ServiceNow development expertise, QA automation capabilities, and general full-stack development skills.

The candidate will handle ServiceNow integration defects, production troubleshooting, automated testing, release management, and internal application maintenance.

Key Responsibilities

• Handle L2 support tickets, escalations, and ServiceNow integration issues.
• Resolve L3 defects involving ServiceNow ITSM/CMDB, Flow Designer, Scripted REST APIs, Business Rules, GlideRecord, and server-side scripting.
• Perform root-cause analysis for production incidents and integration issues.
• Review code and contribute to full-stack development and maintenance.
• Develop and maintain automated tests using Python and Playwright.
• Validate APIs using Postman and data using SQL.
• Manage test cases in Qase and integrate automated tests into CI/CD pipelines.
• Maintain release documentation, Update Sets, XML exports, and release processes.
• Support customer technical escalations and distributed-system debugging.
• Use AI-assisted development and QA tools to improve productivity.

Mandatory Skills

• 6+ years of hands-on ServiceNow development experience.
• ServiceNow ITSM, CMDB, Flow Designer, Scripted REST APIs, Business Rules, GlideRecord, and server-side scripting.
• OAuth2-based integrations and troubleshooting across systems.
• Python and Playwright test automation.
• API testing using Postman and SQL validation.
• React, TypeScript, and API-driven backend development.
• GitHub, CI/CD, and ServiceNow deployment tools.
• Experience with AI-assisted QA tools such as Claude or Codex.

Preferred Skills

• ServiceNow CSA or CIS certifications.
• GitHub Actions and release management experience.
• Semantic versioning and changelog management.
• Customer-facing production support and on-call experience.

Important Conditions

• Immediate joiners only.
• Hybrid work with 2 days in office mandatory.
• Must be available for the 1 PM–11 PM IST shift.
• Must attend the L1 interview in person.
• Six-month contract engagement.

Location: Bangalore (Hybrid)

Experience: 4–8 Years

About the Role

At Twenty20 Systems, we are looking for a Senior Software Engineer (Integration) who can build and support scalable integrations across enterprise applications, APIs, and data systems.

This role is ideal for someone who is hands-on, technically strong, and excited to solve real integration challenges in a fast-paced environment. You will work closely with engineering, product, and delivery teams to build reliable integration solutions while contributing to architecture and engineering best practices.

What You’ll Do

• Design, develop, and maintain enterprise integrations, APIs, and automation workflows across internal and third-party systems.
• Build and manage integrations using Workato and other iPaaS/middleware platforms.
• Develop and optimize API-based services, data pipelines, and workflow automations for business-critical use cases.
• Work with product, engineering, and business teams to understand requirements and build scalable solutions.
• Troubleshoot, monitor, and improve integration performance in production environments.
• Build reusable components, connectors, and automation logic to improve delivery efficiency.
• Ensure integrations follow security, reliability, performance, and governance standards.
• Support delivery teams in resolving integration issues and improving system performance.

Requirements

• 4–6 years of experience in software engineering, middleware, or enterprise integrations.
• Strong hands-on experience in Workato (Boomi, MuleSoft, WSO2, TIBCO experience is a plus).
• Good understanding of REST/SOAP APIs, webhooks, OAuth, and connector-based integrations.
• Strong skills in JSON, XML, SQL, JavaScript/Python, and data transformation logic.
• Experience working with enterprise applications like Salesforce, NetSuite, Workday, Snowflake, Shopify, etc.
• Exposure to cloud platforms (AWS/Azure/GCP) and automation architectures is a plus.
• Good debugging, troubleshooting, and problem-solving skills.
• Ability to work in a fast-paced delivery-oriented environment with strong ownership.

What Your First 3 Months Will Look Like

• Take ownership of assigned integration projects and support ongoing delivery pipelines.
• Work on API integrations, automations, and troubleshooting real-time production scenarios.
• Collaborate with senior engineers and product teams to improve integration quality and performance.

Where You’ll Be in 6 Months

• Independently handling multiple integration workflows and delivery requirements.
• Contributing to reusable frameworks and engineering best practices.
• Becoming a key technical contributor for integration design and production support.
• Driving faster delivery through automation and optimized integration patterns.

We are looking for an integration engineer to assist our rapidly growing customer base. As part of our integration team, you will be the primary point of contact for all integrations. You would be responsible for helping our clients integrate with OneFin APIs, configuring our system for clients, and providing ongoing help to them to resolve any issues.

Application form: https://forms.gle/KbbeXxqhwpwnCzcDA

Responsibilities

• Understand and explain APIs to clients. Help clients integrate OneFin APIs. Research and identify solutions to issues during integration.
• Escalate unresolved issues to appropriate internal teams (e.g., software developers).
• Become a product expert for clients.
• Configure the OneFin system for customized usage by clients. Identify and write internal and external technical articles or knowledge-base entries, like typical troubleshooting steps, workarounds, or best practices, how-to guides, etc.
• Automate the solution of common issues using Python.
• Help live clients resolve issues and coordinate with the development team for issue resolution.

Requirements and Qualifications:

• BS/B.Tech. in Computer Science, Engineering, or an equivalent.
• Strong verbal and written communication skills.
• Experience in writing code in Python.
• Understanding web-based systems.
• Proficient in understanding and writing JSON.
• Experience in SQL databases.
• Experience working with REST APIs.
• Excellent analytical skills, passion for pinning down technical issues, and solving problems.
• Enjoys a fast-paced startup environment.

EMBEDDED AI ENGINEERING POD

AI Implementation Engineer Role

Level: AI Implementation Engineer Senior / Advanced - 6+ years

Practice: Wissen GenAI

Locations: Mumbai / Bengaluru / New York - hybrid, embedded with delivery teams

Reports to: EMBEDDED AI PRACTICE Senior AI Engineering Specialist (Architect); Wissen GenAI Program Lead

Embedded inside enterprise delivery teams, you work closely with global, cross-regional teams to turn prioritized GenAI use cases into production software - building, integrating, and hardening Azure-based AI solutions and accelerating adoption within the teams you join.

You deliver production software and help the teams you join work faster.

As an embedded AI Implementation Engineer, you help convert prioritized use cases into shipped, governed, measurable software.

Key responsibilities

1. Build and ship.

Implement GenAI features end to end on Azure - RAG pipelines, agents, APIs, and UI integrations - against enterprise systems and data.

2. Embed and enable.

Work inside the delivery pods: pair with their engineers, remove blockers, and transfer GenAI skills so adoption sticks after you move on.

3. Productionize.

Add evaluation, observability, guardrails, caching, and CI/CD so prototypes become reliable, cost-efficient services.

4. Integrate securely.

Connect to enterprise data with correct access control, secrets management, and compliance with enterprise security standards and handling of sensitive data.

5. Iterate on quality.

Use evaluation results and user feedback to improve grounding, accuracy, latency, and cost.

6. Measure.

Track delivery and quality metrics that roll up to the program's targets.

Must-have qualifications

• 6+ years in software engineering, with 2+ years building GenAI/LLM applications in production.
• Strong Python (incl. async) and Java (the primary enterprise application stack; Spring a plus); solid API and systems design.
• Azure GenAI hands-on: Azure OpenAI, Azure AI Foundry, Azure AI Search for RAG, Azure AI Document Intelligence (IDP), and Prompt Flow.
• Agent frameworks: Microsoft Agent Framework / Semantic Kernel / AutoGen (or LangChain / LangGraph) and tool / function calling.

Preferred

• RAG fundamentals: embeddings, chunking, vector search, reranking, and grounding.
• Data platforms: Snowflake including Cortex AI (Cortex Search, LLM functions) and SQL, for accessing and grounding on enterprise data.
• Prompt engineering as versioned code; building and running evaluations.
• DevOps: Azure DevOps / GitHub Actions, Docker, AKS / Azure Functions, and observability.
• Financial services or other regulated environments.
• Front-end (React) for AI-assisted UX; streaming and token level operations.
• Azure AI Content Safety and responsible-AI practices.
• Certification: Azure AI Engineer Associate.

What success looks like - first 6 to 12 months

• Multiple GenAI features shipped to production within the embedded delivery pods.
• Measurable adoption and productivity uplift in the teams you support.
• Reusable components adopted from the architects' reference framework.
• Clear contribution to faster time-to-market and lower defect rates.

Role: Senior Fullstack Engineer 

Experience: 8 -12 Years 

Location: Bangalore, Hyderabad, Chennai, Mumbai, and Ahmedabad 

Work Mode: Hybrid (2 days work from office is mandate) 

Budget: 2.5 LPM. 

Notice: Immediate Contract: 6 months 

Shift: 1 PM to 11 PM IST L1 

Interview: In-person (mandate) 

Role Summary 

We're looking for a versatile engineer who combines full-stack and QA skills with ServiceNow platform depth. This role owns ServiceNow integration defects and triage, contributes across the product development lifecycle (code, QA, release), and supports customers and general maintenance work outside the ServiceNow surface. 

Key Responsibilities 

Q1 — Support / Triage / Defects

• L2 triage: act as first point of contact for incoming support/escalation tickets, including ServiceNow platform and integration issues

• L3 defect resolution: own defect fixes on ServiceNow ITSM/CMDB integrations — Flow Designer workflows, Scripted REST APIs, GlideRecord/server-side scripts, OAuth2-based auth — as well as general application-layer defects

• Root-cause analysis: diagnose production integration incidents, including version-drift across releases 

Q2 — Product Development

• PR review: review peers' code, spanning ServiceNow and general full-stack changes

• QA engineering: write and maintain automated tests (Python/Playwright), validate APIs (Postman) and data (SQL), manage test cases in Qase, use AI tools to accelerate test creation and automation

• Release management: maintain internal release documentation, manage Update Sets/XML exports across environments, integrate tests into CI/CD (GitHub Actions), and contribute to the release-train cadence and DORA metrics 

Q3 — Customer Engineering

• Customer developer support: on-call with customers to diagnose and resolve reported issues, including ServiceNow-integration-related problems

• Technical maintenance: take on ad-hoc technical/maintenance work as a general full-stack developer (React/TypeScript, API-driven backend) — internal maintenance only, not customer-facing development 

Required Qualifications

• 6+ years hands-on ServiceNow development: Flow Designer, Scripted REST APIs, Business Rules, server-side scripting

• Working knowledge of CMDB / ITSM data model and OAuth2-based integration patterns

• Experience with AI-assisted QA tooling (Claude, Codex, or similar)

• Hands-on QA/test automation experience (Python, Playwright) and API/data validation (Postman, SQL)

As a Software Engineer, you will tackle a highly challenging role within our Cyber Security Engineering integration development team. You will be responsible for the End-to-End (E2E) delivery of projects from gathering customer requirements to deploying high-performance data exchange solutions on-prem or in the cloud. We are looking for a Systems-Level Thinker who can seamlessly transition between building complex integrations and architecting high-throughput, low-latency backends. Because each integration may require a different technical approach, we value engineers who love reskilling, following agile best practices, and maintaining an evolving mindset.

Core Responsibilities

Customer and Stakeholder Collaboration

• Customer Delight: Maintain a strong customer-first attitude while designing, building, and deploying products.
• Technical Communication: Articulate complex technical trade-offs clearly to both business stakeholders and engineering teams.
• Pipeline Management: Manage customer relationships effectively to ensure a continuous flow of project scope, maintaining clear team backlogs.

Job Requirements

• Experience: 3+ years of proven software engineering experience, with a track record of leading small engineering teams or mentoring junior devs.
• High-Throughput Backend Focus: 3+ years of experience engineering robust, real-time backends specifically using Go (Golang) or Java.
• Polyglot and Integration Capability: Deep expertise in at least one or two additional languages/frameworks from our ecosystem: Python, TypeScript, Node.js, React.js, Angular, C#, or C++.
• Computer Science Fundamentals: Excellent knowledge of complex Data Structures, algorithms, and their correct situational usage.
• Adaptability: A passionate programmer who is genuinely open and excited to learn new software development skills as required by changing project demands.

Bonus / Added Advantages

• AI & Intelligent Automation: Hands-on knowledge of AI concepts, Large Language Models (LLMs), and experience building or integrating AI agents to automate complex workflows or security processes.
• Cybersecurity Domain: Experience in the cybersecurity space (SIEM, SOAR, or platform security).
• Modern Web Tech: Strong experience in developing software using modern web technologies.
• E2E Lifecycle: Proven background handling a project completely from discovery to cloud/on-prem deployment.
• Open Source: Active contributions to the Open Source community. (Please share your GitHub/GitLab links in your application)

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.

Position: Senior/Lead Full Stack Engineer – Gen AI / Agentic AI

Experience: 7+ Years

Employment: Permanent Position

Location: Banglore / Hyderabad

Job Summary

We are looking for a Senior/Lead Full Stack Engineer – Gen AI / Agentic AI with strong hands-on experience in Python, React.js, MongoDB, Java/Spring Boot and Generative AI/Agentic AI.

The candidate should have experience designing and developing scalable enterprise applications and implementing production-grade LLM, RAG, AI Agent and multi-agent solutions.

Key Responsibilities

• Design, develop and maintain scalable full-stack applications using Python, React.js, MongoDB and Java/Spring Boot.
• Build production-grade Generative AI and Agentic AI applications using LLMs and modern AI frameworks.
• Develop RAG pipelines, AI agents, tool calling, memory management, planning and agent orchestration.
• Work with LangChain, LangGraph, MCP, vector databases and semantic search.
• Develop Python-based APIs, microservices and asynchronous applications using FastAPI/Flask/Django.
• Build REST APIs and event-driven microservices with focus on scalability, performance and resilience.
• Integrate LLMs, embeddings, vector stores and external enterprise tools/services.
• Implement prompt engineering, LLM evaluation, guardrails and AI observability.
• Develop responsive front-end applications using React.js.
• Work with MongoDB, SQL and hybrid data models.
• Implement CI/CD pipelines and support cloud/OCP deployments.
• Follow secure coding, testing, code quality and performance best practices.
• Participate in architecture, technical design, code reviews and mentoring of team members.
• Collaborate with business and technical stakeholders to translate requirements into scalable solutions.

Mandatory Skills

1. Python
2. React.js
3. Gen AI / Agentic AI
4. RAG + LLM
5. LangChain / LangGraph
6. MongoDB
7. Java + Spring Boot
8. REST APIs / Microservices
9. Vector Databases / Embeddings
10. MCP / AI Agent orchestration

Good to Have

• FastAPI / Flask / Django
• Kafka / Solace
• Docker / Kubernetes
• AWS / Azure / GCP / OCP
• CI/CD – Jenkins / GitHub Actions
• LLMOps / AI evaluation / observability
• ELK / Grafana / Splunk / AppDynamics
• SQL / NoSQL
• Agile/Scrum

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.