Golang Backend Engineer

AI / Backend Engineer — Remote (India / Overseas)

Company: Ink'd

Compensation: $1,500–$2,500 USD/month, depending on experience

Type: Independent contractor, long-term opportunity

Ink'd is building a voice-first AI platform for residential real estate transactions. Our AI assistant allows real estate agents to generate contracts, automate workflows, and complete transaction documentation through natural conversation.

We're looking for a highly capable AI/backend engineer to help scale and maintain our production platform.

Responsibilities

• Build and maintain production AI agent workflows
• Develop integrations with third-party APIs and services
• Work with LLMs, structured data extraction, and document generation
• Troubleshoot production systems and improve reliability
• Build scalable backend services and maintain databases
• Collaborate directly with the founder and engineering team

Technical experience

• Python, FastAPI, Node.js, and REST APIs
• LLM APIs, tool calling, and agent orchestration
• PostgreSQL and cloud infrastructure
• GitHub, CI/CD, debugging, and deployment
• Experience with voice AI, WebSockets, or document processing is a major plus

What we're looking for

Someone who has independently shipped production software, can troubleshoot unfamiliar systems, communicates clearly, and takes ownership of their work.

Please include your GitHub, two examples of production systems you've built, your specific contributions, monthly compensation expectations, and availability.

About the Role

We’re building the next generation of AI-powered business software, and we’re looking for people who want to shape that future with us. With Lumen, we’re reimagining how users interact with CRM — moving beyond screens, menus and dashboards to an intelligent interface where users can simply ask AI to take actions, retrieve knowledge, generate insights and get work done. With Agent Studio, we’re enabling businesses to build, test and deploy their own AI agents for real-world workflows. And with Invorto, we’re bringing AI to voice, allowing businesses to create intelligent voice agents tailored to their customer and operational use cases.

What makes this especially exciting is the stage and scale of the opportunity. You’ll get to work on genuinely hard problems across LLMs, agents, reasoning, orchestration, voice AI, evaluation, reliability and enterprise security — not as isolated experiments, but as products used in real business workflows. You’ll have the opportunity to build zero-to-one, own meaningful parts of the product end-to-end, work closely with customers, experiment rapidly, and see your work reach production at scale.

Why join now? Because the playbook for enterprise AI is still being written. You won’t just be implementing someone else’s roadmap — you’ll help define the product, architecture and experiences that become that playbook. Expect high ownership, fast iteration, hard technical and product problems, direct customer impact, and the chance to build AI systems that have to work reliably in the real world — not just in a demo.

About the Role

We are looking for a Senior AI/ML Backend Engineer to help build the core intelligence layer powering Lumen and Agent Studio. You will design and ship production-grade backend systems that integrate LLMs into real agentic workflows — taking actions, retrieving knowledge and generating insights inside a live CRM product used by real businesses. This is a hands-on, build-focused role with direct ownership of systems that ship to production.

What You’ll Do

• Design, build and scale backend services in Python that power LLM-driven and agentic features within Lumen and Agent Studio.
• Build and productionize agentic AI systems — including planning, tool use, orchestration, memory and multi-step task execution.
• Integrate LLMs into core product workflows, focusing on reliability, latency, cost and correctness at production scale.
• Build robust APIs and services that connect AI agents with CRM data, business logic and third-party systems.
• Own evaluation, testing and monitoring for AI features to ensure they behave reliably in real-world, not just demo, conditions.
• Collaborate closely with product, design and other engineers to take features from zero to one and iterate rapidly based on real usage and customer feedback.
• Work directly with customers and customer-facing teams to understand real workflows, debug issues and translate feedback into product and engineering decisions.

What We’re Looking For

• 2–4 years of professional backend engineering experience, with strong hands-on Python skills.
• Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
• Should be hands-on with traditional Machine learning frameworks like Pytorch, Scikit-learn
• Solid understanding of API design, backend architecture, databases and distributed systems fundamentals.
• Familiarity with LLM orchestration concepts — prompting, tool/function calling, RAG, agent frameworks, evaluation and guardrails.
• Comfort working in a fast-paced, ambiguous, zero-to-one environment where you’ll be defining as much as building.
• Strong communication skills — this is a customer-facing role, and you will be expected to clearly articulate technical concepts, decisions and trade-offs to both technical and non-technical stakeholders, including customers.

Good to Have

• Experience with enterprise security, reliability or observability practices for AI systems.
• Prior experience working on CRM, SaaS or other enterprise business software.
• Exposure to voice AI or real-time systems.

Proficient

Python Backend Development

Python, REST APIs, Microservices, FastAPI, Flask, Django, API Design, Authentication, Error Handling

Design, implement, and maintain scalable backend systems using Python frameworks. Build robust REST APIs, ensure secure authentication, and handle errors gracefully. Demonstrate expertise in microservices architecture and API design.

35

Proficient

React JS

React JS, Components, Hooks, State Management, API Integration, Responsive UI, Frontend Testing

Develop responsive and interactive user interfaces using React JS. Effectively use components, hooks, and state management. Integrate APIs and ensure frontend code is well-tested and maintainable.

30

Proficient

Gen AI / Agentic AI

LLMs, Agentic AI, LangChain, LangGraph, Google ADK, Semantic Kernel, Prompt Engineering, Tool Calling, RAG, Embeddings, Vector Search, Memory, Planning, Reasoning, Multi-Agent Systems, Guardrails, Azure OpenAI, Vertex AI, AWS Bedrock

Demonstrate hands-on experience with LLMs and agentic AI frameworks. Build, integrate, and optimize AI agents using modern tools and platforms. Show proficiency in prompt engineering, tool calling, RAG, embeddings, and multi-agent systems.

30

Advanced

Cloud & Deployment

AWS, Azure, GCP, Cloud Deployment, Containers, CI/CD, Application Hosting, Monitoring

Deploy applications to cloud platforms, manage containers, set up CI/CD pipelines, and monitor application health. Demonstrate familiarity with at least one major cloud provider.

Senior AI Engineer

Code Generation, Agent Architecture & LLM Systems

📍 Mumbai (On-site) | Full-time | 5+ years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.

The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.

The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.

You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.

Responsibilities:

Agent Architecture

Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.

Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.

Code Generation Pipeline

Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.

Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.

Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.

Self-Repair Loop

Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.

This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.

Context Management

Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.

Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.

Own token budgeting and prompt caching strategy.

Prompt Engineering as a Discipline

Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).

Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.

Version prompts like code with changelogs and rollback capability.

Evaluation and Quality Measurement

Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.

Define regression gates that block quality-degrading changes from shipping.

Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.

This responsibility is non-negotiable at this level.

Model Strategy and Cost

Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.

Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.

Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.

Safety and Reliability of Agent Behaviour

Defend against prompt injection from user content and fetched web content.

Ensure secrets never appear in generated client code.

Define what the agent's tools may and may not do in collaboration with the platform team.

Contribute to output moderation and abuse-pattern awareness.

Mentorship and Engineering Standards

Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.

Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.

Requirements:

Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)

Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.

POCs, internal demos, and tutorial-grade work do not qualify.

5+ Years of Professional Software or AI Engineering Experience

With at least 3 years focused on LLM applications, AI engineering, or production AI systems.

Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.

Strong Python Proficiency and Service Development

Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.

Not notebook-only.

Depth Across LLM APIs and Agent Systems

Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).

Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Hands-on with tool calling, structured outputs, and multi-step reasoning.

Demonstrated, Systematic Evaluation Practice - Non-Negotiable

Must have built evaluation harnesses that gate production releases, not ad-hoc testing.

Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.

Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.

Cost Discipline for Production AI

Track record of measurable cost optimisation on production AI features.

Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.

AWS Working Knowledge

Hands-on with EC2, S3, IAM, and Docker.

Comfort with CI/CD workflows and deploying AI services.

Awareness of LLM Security Failure Modes

Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.

Nice to Have

• Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
• MCP server authoring
• Open-source AI contributions
• Published technical writing on LLM systems
• Multi-modal model experience
• Fine-tuning exposure (LoRA, QLoRA, PEFT)

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 PortOne

PortOne is building the reconciliation and data intelligence layer for payments across Korea and international markets. We are a Series B startup backed by Softbank and Hanwa Capital, powering multi-billion dollars in annualised settlement volume for 2,000+ merchants across Korea, Thailand, Singapore, Indonesia, and beyond.

We are building AI-native products for leading brands — intelligent automation layers on top of complex financial data pipelines. If you want to work at the intersection of fintech, data engineering, and applied AI, this is your role.

Culture and Values

* You will be joining a team that stands for making a difference.

* You will be joining a culture that identifies more with Sports Teams rather than a 9 to 5 workplace.

* Your will have peers who are/have

** Highly Self Driven with A sense of purpose

** High Energy Levels - Building stuff is your sport

** Ownership - Solve customer problems end to end - Customer is your Boss

** Hunger to learn - Highly motivated to keep developing new tech skill sets

Your Work Ethic

* You are an athlete and building apps is your sport.

* Your passion drives you to learn and build stuff and not because your manager tells you to.

* You obsess over correctness — a bug in a settlement figure or a silent data drop is not acceptable to you.

* You have an eye for detail that most engineers skip past, and you take pride in getting it exactly right.

* Your work ethic is that of an athlete preparing for your next marathon. Your sport drives you and you like being in the zone.

* You are NOT a clockwatcher renting out your time, and NOT have an attitude of "I will do only what is asked for"

What will you do?

• Build and maintain financial data ingestion pipelines that pull settlement and transaction data from marketplace platforms (Amazon, Shopee, TikTok, Qoo10, Rakuten) on behalf of large brands operating across multiple Asian markets.
• Own reconciliation workflows end-to-end — from raw marketplace data to verified, merchant-ready settlement reports — ensuring every figure is correct and every discrepancy is surfaced, not swallowed.
• Design and implement AI-native features that automate financial analysis: agentic triage of settlement mismatches, root-cause detection across large transaction volumes, and intelligent alerting for ops and merchant teams.
• Instrument data quality and health monitoring so that silent failures — missing records, schema shifts, delayed ingestion — are caught before they reach the merchant.
• Build APIs and tooling that enable PortOne's ops and merchant success teams to investigate, verify, and close financial discrepancies faster and with more confidence.
• Expand platform coverage by integrating new marketplaces and new report types, working closely with data formats that are often inconsistent, undocumented, or changing without notice.
• Uphold rigorous engineering standards — correctness in financial data is not negotiable, and you treat edge cases and off-by-one errors with the same seriousness as a production incident.
• Uphold high engineering standards across codebases and processes.
• Collaborate with product, design, infrastructure, and operations stakeholders.

Skills and Experience

* Have ideally 4 to 6 Years of experience shipping high quality products/live features and workflows

*AI-native development — you build products where AI is a first-class component, not a bolt-on.

* Strong backend engineering foundation — Go (Preferred), Python, or equivalent; REST/gRPC APIs; database design.

* Understands how to build scalable, resilient, and observable distributed systems.

* Must have built data flows and applications end to end taking full ownership

Preferred Skills and Background

*Prior experience/built apps in golang backend

*Data and data engineering background — comfortable with data pipelines, ETL/ELT patterns, event-driven architectures, reconciliation logic, or analytical workloads.

*AI agentic development — experience building or working with agent frameworks, tool-use patterns, LLM orchestration, or automated reasoning pipelines.

Job Overview

Architect and build scalable, high-performance backend systems while working on mission-critical platforms that process real-time market data and portfolio analytics. The role also involves leveraging Generative AI capabilities to enhance data intelligence, automation, and user-facing features, while ensuring regulatory compliance and secure financial transactions.

Key Responsibilities

• Design, develop, and maintain scalable backend services and APIs using NodeJS and Python
• Build event-driven architectures using RabbitMQ and Kafka for real-time data processing
• Develop and manage data pipelines integrating PostgreSQL and BigQuery for analytics and warehousing
• Integrate and deploy Generative AI models (LLMs, embeddings, AI APIs) into backend systems for automation, insights, and intelligent workflows
• Design AI-powered features such as recommendation systems, document processing, or conversational interfaces
• Ensure system reliability, security, and low-latency performance for mission-critical systems
• Lead technical design discussions, conduct code reviews, and mentor junior engineers
• Optimize database queries, implement caching strategies, and improve overall system performance
• Collaborate with cross-functional teams to deliver end-to-end product features
• Implement monitoring, logging, and observability solutions

Required Skills and Qualifications

• 2+ years of professional backend development experience
• Strong expertise in NodeJS and Python for production-grade applications
• Proven experience building RESTful APIs and microservices architectures
• Experience working with Generative AI frameworks/APIs (OpenAI, LangChain, vector databases, prompt engineering)
• Understanding of integrating LLMs into production systems (RAG, embeddings, fine-tuning basics)
• Strong proficiency in PostgreSQL, including query optimization and schema design
• Hands-on experience with RabbitMQ and Kafka
• Experience with BigQuery or similar data warehousing solutions
• Solid understanding of distributed systems, scalability patterns, and high-traffic applications
• Strong knowledge of authentication, authorization, and security best practices
• Experience with Git, CI/CD pipelines, and modern development workflows
• Excellent problem-solving and debugging skills
• Exposure to fintech or financial services, cloud platforms (GCP/AWS/Azure), Docker/Kubernetes, caching tools (Redis/Memcached), and regulatory requirements (KYC, compliance, data privacy) is a plus

Apply directly at: https://wohlig.keka.com/careers/jobdetails/136351

About Orenda

Orenda is a fintech-enabled financial services company founded in 2017 and based in Ahmedabad, Gujarat. We work as a corporate DSA and channel partner for leading banks and NBFCs, helping individuals, professionals, and MSMEs access suitable financial solutions across business loans, personal loans, home loans, loan against property, and professional loans.

At Orenda, we combine financial expertise, technology, and streamlined processes to simplify the lending journey. We are building our own lending-tech platform to improve operational efficiency, strengthen lender and partner integrations, and develop technology-driven solutions that support our evolving business needs.

About the role

We're looking for a Lead Software Engineer who enjoys building as much as designing. You'll shape how our systems are architected, write a good share of the code yourself, and lead a team of 3 to 5 engineers, including AI/ML engineers.

This is a hands-on leadership role, not a pure management or architecture position. Your time will be roughly split like this:

Share of time

Leading the team ~30%

Solution design ~30%

Hands-on coding ~30%

Deployment and infrastructure~10%

What you'll work on

Solution design and architecture

• Turning business needs into technical designs that are practical to build and easy to maintain

• Making architecture decisions across services, integrations and data flows

• Estimating, scoping and planning new features with business and product stakeholders

• Shaping the solution design for AI/ML use cases alongside the AI/ML engineers

Engineering and delivery

• Writing production code in Python and Node.js

• Rolling out new features across our internal and partner-facing platforms, including our CRM and partner portal

• Continuously improving existing systems for performance, reliability and code quality

• Reviewing code and setting engineering standards for the team

Infrastructure and cloud

• Maintaining and upgrading our infrastructure, including a planned move to a major cloud platform

• Owning deployments and production stability together with the team

• Working with event-driven and messaging systems (NATS today, Kafka planned)

Team leadership

• Leading and mentoring a team of 3 to 5 engineers

• Planning and prioritising work, unblocking the team, and making sure things ship

• Building a culture of ownership, clear documentation and quality

What we're looking for

• 5 to 8 years of software engineering experience, with hands-on coding in your recent roles

• Strong command of Python and Node.js

• Proven experience designing and building backend systems and microservices that run in production

• Hands-on experience deploying and running workloads on at least one major cloud platform (AWS, Azure or GCP)

• Solid working knowledge of PostgreSQL

• Experience with messaging or event streaming systems such as NATS, Kafka or RabbitMQ

• Experience leading or mentoring a small engineering team

• Comfort estimating effort, breaking down features and planning delivery

• Enough understanding of AI/ML systems to guide their solution design and integration

• Clear communication with both technical and non-technical stakeholders

Good to have

• Experience working at a fintech, lending or financial services company

• Hands-on experience with Kafka

• Experience leading an infrastructure upgrade or cloud migration

• Experience building or scaling CRM or partner-facing portal products

Why Join Us?

• Build a growing lending-tech platform: Play a key role in shaping the technology behind a growing financial services business.
• Own technical decisions: Contribute directly to architecture, engineering standards, and technology direction.
• Stay hands-on: Combine solution design and technical leadership with meaningful coding responsibilities.
• Work with emerging technologies: Collaborate with AI/ML engineers and contribute to evolving platform capabilities.
• Lead and grow a team: Mentor engineers and help build a strong engineering culture as the technology function scales.
• Make a measurable impact: Build systems that improve operational efficiency and support business growth.

We’re seeking a highly skilled, execution-focused Senior Backend Engineer with a minimum of 5 years of experience to join our team. This role demands hands-on expertise in building and scaling distributed systems, strong proficiency in Java, and deep knowledge of cloud-native infrastructure. You will be expected to design robust backend services, optimize performance across storage and caching layers, and enable seamless integrations using modern messaging and CI/CD pipelines.

You’ll be working in a high-scale, high-impact environment where reliability, speed, and efficiency are paramount. If you enjoy solving complex engineering challenges and have a passion for distributed systems, this is the right role for you.

Responsibilities—

• Design, develop, and maintain distributed backend systems at scale.
• Write high-performance, production-grade code in Java.
• Architect and optimize storage systems, ensuring efficient query performance and scalable data models.
• Implement caching strategies to reduce latency and improve system throughput.
• Build and manage services leveraging AWS cloud infrastructure.
• Develop resilient messaging pipelines using Kafka (or equivalent) for real-time data processing.
• Define and streamline CI/CD pipelines, ensuring rapid and reliable deployment cycles.
• Collaborate with product managers, frontend engineers, and DevOps to deliver end-to-end solutions.
• Monitor system performance, identify bottlenecks, and apply proactive fixes.
• Drive best practices in software engineering, testing, and code reviews. 

Requirements—

• 5+ years of experience in backend engineering, with deep hands-on coding experience.
• Strong proficiency in Java and familiarity with modern frameworks.
• Proven track record in building scalable distributed systems.
• Hands-on expertise with AWS services (e.g., EC2, S3, Lambda, DynamoDB, RDS).
• Solid understanding of messaging systems like Kafka, RabbitMQ, or similar.
• Strong grasp of query performance optimization and storage system design.
• Experience with caching solutions (Redis, Memcached, etc.).
• Familiarity with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, etc.).
• Excellent problem-solving skills and ability to thrive in fast-paced environments.
• Strong communication and collaboration skills, with a proactive mindset.

Benefits—

• Best in class salary: We hire only the best, and we pay accordingly.
• Proximity Talks: Meet other designers, engineers, and product geeks — and learn from experts in the field.
• Keep on learning with a world-class team: Work with the best in the field, challenge yourself constantly, and learn something new every day.

About us—

Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.

Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.

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.