Senior AI Engineer

Company Description

Recruiting Bond International is a next-generation Talent Intelligence, Executive Search, and Human Capital Advisory firm helping start-ups, enterprises, GCCs, and VC/PE-backed companies build high-impact global teams. It is a global leader in Recruitment Process Outsourcing (RPO), executive search, and workforce consulting, specializing in building transformative talent strategies.

From high-growth startups to Fortune 500 companies, Recruiting Bond partners with organizations across 50+ industries and 140+ countries to deliver fast, scalable, and inclusive hiring solutions. The company supports businesses in scaling teams, fostering innovation, and creating talent-first strategies to achieve their goals.

With deep expertise across Technology, FinTech, Healthcare, Real Estate, and Energy, Recruiting Bond is dedicated to building careers, companies, and futures by connecting world-class talent with high-impact opportunities globally.

About the Role

Our client is hiring a Backend Engineer (India-based, Remote) to design, build, and scale the core memory infrastructure powering production-grade AI agents.

This role is intended for an experienced engineer with 7–10 years of backend engineering experience, who has deeply internalized AI-native engineering practices and actively builds using tools such as Claude Code, Codex, Cursor, Windsurf, or comparable AI development tools as a core part of their workflow.

The hiring process is intentionally non-traditional and skill-first. There is no evaluation based on IIT pedigree, LeetCode performance, or conventional resume filters. Instead, the only evaluation criterion is: how you build with AI in real-world scenarios.

Candidates are expected to submit prompt logs or transcripts from Claude Code, Codex, Cursor, or Windsurf demonstrating a feature or product they are proud of.

What You'll Own

• Build and scale backend systems powering the memory infrastructure of the product
• Own and deliver features end-to-end, integrating AI coding tools into the core development workflow
• Design, manage, and optimize database, storage, and retrieval systems for persistent memory
• Collaborate closely on system architecture, scalability, performance, and reliability engineering
• Contribute directly to product roadmap decisions based on real customer usage and production insights

Requirements

Must-Have

• 7–10 years of backend engineering experience
• Demonstrated ability to build with AI coding tools (Claude Code, Codex, Cursor, Windsurf, or comparable)
• Ability and willingness to submit prompt log transcripts from a feature or product you are proud of
• Strong Python fundamentals
• Strong PostgreSQL or comparable relational database fundamentals
• Comfort owning systems end-to-end in production
• Based in India, remote work from anywhere in the country

Nice-to-Have

• Prior AI infrastructure or developer tools product experience
• FastAPI fluency
• Open-source contributions in AI, memory, vector databases, or developer tools
• Prior experience in memory systems, RAG pipelines, or vector database engineering
• Public technical writing or conference talks on AI-native engineering practices

Location: Mumbai, Maharashtra, India

Sector: Technology, Information & Media

Company Size: 500 - 1,000 Employees

Employment: Full-Time, Permanent

Experience: 10 - 14 Years (Engineering Leadership)

Level: Engineering Manager / Group EM

ABOUT THIS MANDATE :

Recruiting Bond has been exclusively retained by one of India's most prominent and well-established digital platform organisations operating at the intersection of Technology, Information, and Media to identify and place an exceptional Engineering Manager who can lead engineering teams through an enterprise-wide AI adoption and digital transformation agenda.

This is a high-impact, hands-on leadership role at the nexus of people, product, and technology. The organisation is executing one of the most ambitious AI transformation programmes in its sector and this Engineering Manager will be a core driver of that change. You will lead multiple squads, own engineering delivery end-to-end, embed AI tooling and practices into the team's DNA, and shape the engineering culture of tomorrow.

We are seeking leaders who code when it matters, who build systems and teams with equal conviction, and who view AI not as a trend but as a fundamental shift in how great software is built.

THE OPPORTUNITY AT A GLANCE :

AI-First Engineering Culture :

• Own AI adoption across your squads - from LLM tooling integration to automation-first delivery workflows. Make AI a default, not an afterthought.

Hands-On Engineering Leadership :

• Stay close to the code. Lead architecture reviews, unblock engineers, and set the technical bar - not just the management agenda.

People & Org Builder :

• Grow engineers into leaders. Build squads of 615 across functions. Drive hiring, career frameworks, and a culture of psychological safety.

KEY RESPONSIBILITIES :

1. Hands-On Technical Engagement :

• Remain deeply embedded in the technical work participate in design reviews, architecture decisions, and critical code reviews
• Set and uphold the engineering quality bar : performance benchmarks, security standards, test coverage, and release quality
• Provide technical direction on backend platform strategy, API design, service decomposition, and data architecture
• Identify and resolve systemic technical debt and architectural risks across team-owned services
• Unblock engineers by diving into complex problems debugging, pair programming, and system analysis when it matters
• Own key technical decisions in collaboration with Tech Leads and Principal Engineers; balance pragmatism with long-term sustainability

2. AI Adoption, Integration & Transformation (2026 Mandate) :

• Define and execute the team's AI adoption roadmap - from developer tooling to product-facing AI features
• Champion the integration of GenAI tools (GitHub Copilot, Cursor, Claude, ChatGPT) across the full engineering workflow coding, testing, documentation, incident response
• Embed LLM-powered capabilities into the product : recommendation engines, intelligent search, conversational interfaces, content generation, and predictive systems
• Lead evaluation and adoption of AI-assisted SDLC practices : automated code review, AI-generated test suites, intelligent observability, and anomaly detection
• Partner with Data Science and ML Platform teams to productionise ML models with robust MLOps pipelines
• Build team literacy in prompt engineering, RAG (Retrieval-Augmented Generation), and AI agent frameworks
• Create an experimentation culture : run structured AI pilots, measure productivity impact, and scale what works
• Stay ahead of the AI tooling landscape and advise senior leadership on strategic AI investments and engineering implications

3. People Leadership & Team Development :

• Lead, manage, and grow squads of 6 - 15 engineers across seniority levels (L2 through L6 / Junior through Staff)
• Conduct structured 1 : 1s, career growth conversations, and development planning with every direct report
• Design and execute personalised AI upskilling programmes ensure every engineer develops practical AI fluency by end of 2026
• Build and maintain a high-performance team culture : clarity of ownership, accountability, fast feedback loops, and psychological safety
• Drive performance management fairly and rigorously recognise top performers, manage underperformance constructively
• Lead technical hiring end-to-end : define job requirements, conduct bar-raising interviews, and make data-driven hire decisions
• Contribute to engineering career frameworks and level definitions in partnership with the VP / Director of Engineering

4. Engineering Delivery & Execution Excellence :

• Own end-to-end delivery for multiple product squads from planning and scoping through production release and post-launch stability
• Implement and refine agile delivery frameworks (Scrum, Kanban, Shape Up) calibrated to squad needs and product cadence
• Drive predictable delivery : maintain healthy sprint velocity, manage WIP limits, and ensure dependency resolution across teams.
• Establish and own engineering KPIs : DORA metrics (deployment frequency, lead time, MTTR, change failure rate), uptime SLOs, and velocity trends
• Lead incident management : build blameless post-mortem culture, own RCA processes, and drive systemic reliability improvements
• Balance technical debt repayment with feature velocity negotiate prioritisation transparently with Product leadership

5. Strategic Leadership & Cross-Functional Influence :

• Serve as the primary engineering partner for Product, Design, Data, and Business stakeholders translate ambiguity into executable engineering plans
• Participate in quarterly roadmap planning, capacity forecasting, and OKR definition for engineering teams
• Represent engineering in leadership forums articulate technical constraints, risks, and opportunities in business terms
• Contribute to org-wide engineering strategy : platform investments, build-vs-buy decisions, and shared infrastructure priorities
• Build relationships across geographies (Mumbai HQ + distributed teams) to maintain alignment and delivery cohesion
• Act as a culture carrier and ambassador for engineering excellence, innovation, and responsible AI use

AI TRANSFORMATION LEADERSHIP 2026 EXPECTATIONS :

In 2026, Engineering Managers at this organisation are expected to be active architects of AI transformation not passive observers. The following outlines the specific AI leadership expectations for this role :

AI Developer Productivity

• Drive measurable uplift in developer velocity through AI tooling adoption. Target : 30%+ reduction in code review cycle time and 40%+ increase in test coverage automation by Q3 2026.

LLM & GenAI Product Features

• Own delivery of GenAI-powered product capabilities : intelligent content, semantic search, personalisation, and conversational UX in production, at scale.

AI-Augmented Observability

• Implement AI-driven monitoring and anomaly detection pipelines. Reduce MTTR by leveraging predictive alerting, intelligent runbooks, and auto-remediation scripts.

Team AI Fluency :

• Build mandatory AI literacy across all engineering levels.
• Every engineer understands prompt engineering basics, AI ethics guardrails, and responsible AI deployment practices.

Responsible AI Governance :

• Partner with Security, Legal, and Data Privacy to ensure all AI deployments meet compliance standards, bias mitigation requirements, and explainability benchmarks.

TECHNOLOGY STACK & DOMAIN FAMILIARITY REQUIRED :

• Languages: Java/ Go/ Python/ Node.js /PHP /Rust (must be hands-on in at least 2)
• Cloud: AWS / GCP / Azure (multi-cloud exposure strongly preferred)
• AI & GenAI: OpenAI / Anthropic / Gemini APIs /LangChain /LlamaIndex / RAG / Vector DBs / GitHub
• Copilot: Cursor /Hugging Face
• Containers: Docker /Kubernetes /Helm /Service Mesh (Istio / Linkerd)
• Databases: PostgreSQL /MongoDB / Redis / Cassandra / Elasticsearch / Pinecone (Vector DB)
• Messaging: Apache Kafka /RabbitMQ /AWS SQS/SNS /Google Pub/Sub
• MLOps & DataOps: MLflow /Kubeflow / SageMaker / Vertex AI /Airflow /dbt
• Observability: Datadog /Prometheus /Grafana /OpenTelemetry / Jaeger /ELK Stack
• CI/CD & IaC: GitHub Actions ArgoCD / Jenkins / Terraform /Ansible /Backstage (IDP)

QUALIFICATIONS & CANDIDATE PROFILE :

Education :

• B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution - CS, IS, ECE, AI/ML streams strongly preferred
• Demonstrated engineering depth and leadership impact may complement institution pedigree

Experience :

• 10 to 14 years of progressive engineering experience, with at least 3 years in a formal Engineering Manager or equivalent people-leadership role
• Proven track record of managing and scaling engineering teams (615+ engineers) in a fast-growing SaaS or digital product environment
• Hands-on backend engineering background must be able to read, write, and critique production code
• Direct experience driving AI/ML feature delivery or AI tooling adoption within engineering organisations
• Exposure across start-up, mid-size, and large-scale product organisations, preferred adaptability is a core requirement
• Strong CS fundamentals: distributed systems, algorithms, system design, and software architecture
• Demonstrated career stability minimum of 2 years of average tenure per organisation.

The Ideal Engineering Manager in 2026 :

• Leads with context, not control, empowers engineers while maintaining accountability and quality
• Is fluent in both people language and technical language, switches registers naturally with engineers and executives alike
• Sees AI as a force multiplier for the team, not a threat. Actively experiments with and advocates for AI tooling
• Measures success by team outcomes, not personal output. Takes pride in what the team ships, not what they build alone
• Creates feedback loops obsessively between product and engineering, between seniors and juniors, between metrics and decisions
• Has strong opinions, loosely held, brings conviction to discussions but updates on evidence
• Invests in engineering excellence as seriously as delivery velocity knows that quality and speed are not opposites

WHY THIS ROLE STANDS APART :

AI Transformation at Scale :

• Lead one of the most significant AI adoption programmes in India's digital media sector.
• Our decisions will shape how hundreds of engineers work in 2026 and beyond.

Hands-On & Strategic Balance :

• A rare EM role that actively encourages technical depth.
• Stay close to the code while owning the people agenda - the best of both worlds.

Established Platform, Real Scale :

• 5001,000 engineers, proven product-market fit, and the org maturity to execute.
• This is not a greenfield startup gamble it is a serious company with serious ambition.

Clear Leadership Growth Path :

• A visible, direct path toward Director / VP of Engineering.
• Senior leadership is invested in growing its next generation of technology executives.

Mandatory Skills:

• Strong hands-on experience in Python
• Experience in Generative AI / LLMs
• Good understanding of FinTech or Banking domain
• Experience with APIs and Microservices
• SQL/Database knowledge
• Cloud platform experience (AWS/Azure/GCP)
• Strong communication and problem-solving skills

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 TIFIN

TIFIN is an AI-first fintech platform transforming wealth management through data science, machine learning, and intelligent automation. With strong global backing and a rapidly growing India hub, TIFIN is building scalable, next-gen financial products used by global institutions.

Role Overview

We are looking for a Senior Software Engineer with strong backend and AI integration experience to build scalable, high-performance systems. This role involves working closely with product, data science, and AI teams to develop intelligent platforms leveraging modern technologies and LLMs.

Key Responsibilities

• Design, develop, and scale backend systems and APIs using Golang and Python
• Build and integrate AI-driven features, including prompt-based workflows (Claude or similar LLMs)
• Work with MongoDB and Elasticsearch for high-performance data handling and search capabilities
• Optimize system performance, scalability, and reliability
• Collaborate with cross-functional teams (Product, AI/ML, Data Engineering)
• Contribute to architecture decisions and best engineering practices
• Write clean, maintainable, and production-grade code

Required Skills & Experience

• 3–5 years of experience in backend engineering
• Strong proficiency in Golang and/or Python
• Hands-on experience with MongoDB and Elasticsearch
• Experience working with LLMs / AI tools (Claude, OpenAI, etc.) and prompt engineering
• Good understanding of REST APIs, microservices architecture, and distributed systems
• Strong problem-solving and debugging skills

Good to Have

• Experience in fintech / SaaS platforms
• Exposure to AI/ML pipelines or data platforms
• Knowledge of cloud platforms (AWS/GCP/Azure)
• Familiarity with CI/CD and DevOps practices

About Techjays

At Techjays, we build production-grade AI platforms for global clients. We operate at the intersection of backend engineering, distributed systems, and applied AI — delivering secure, scalable, and enterprise-ready intelligent systems. Our team has built and scaled products at Google, Akamai, NetApp, ADP, Cognizant, and Capgemini.

About the Role

This is not a feature-delivery role. We are looking for an AI Lead who can architect, own, and scale intelligent backend systems end-to-end. You will drive both technical direction and execution — working across LLM integrations, RAG pipelines, agentic AI workflows, and cloud-native backend systems for global clients.

What You'll Do

• Architect and scale backend systems powering AI-driven applications
• Design and implement RAG pipelines, AI agents, and LLM integrations
• Own systems end-to-end — from architecture to deployment and scaling
• Integrate and optimize LLMs (Claude, GPT, Gemini) for real-world production use cases
• Build high-performance distributed systems with observability and cost efficiency
• Lead backend and AI initiatives with strong technical ownership
• Mentor engineers and raise the technical bar across teams
• Collaborate with product and AI teams to deliver AI-native solutions

What We're Looking For

• 6–10 years of strong backend engineering experience
• Hands-on expertise in Python (FastAPI / Django / Flask)
• Deep understanding of Generative AI and LLM-based systems
• Strong experience with RAG pipelines and Vector Databases (Pinecone, FAISS, ChromaDB, Weaviate)
• Solid knowledge of Agentic AI — building autonomous agents and multi-agent workflows
• Proficiency in AWS or GCP in production environments
• Experience with distributed systems, microservices, and system design
• Strong grasp of Data Structures, Algorithms, and Design Patterns
• Familiarity with WebSockets, Git, Linux/Unix, and CI/CD

Nice to Have

• Experience with Anthropic Claude API and Claude Code
• Familiarity with real-time data systems or streaming (Kafka, etc.)
• MLOps and AI system lifecycle experience
• Optimizing AI systems for latency, cost, and scalability

Who You Are

• You think in systems, not just features
• You take full ownership of what you build
• You are comfortable navigating fast-moving, ambiguous environments
• You stay updated with the latest in Generative AI and backend technologies
• Strong communicator who can collaborate across teams and global clients

What We Offer

• Competitive compensation (Best in Industry)
• Work on production-grade AI systems used by global clients
• Exposure to cutting-edge AI tools and frameworks
• A culture that values clarity, integrity, and continuous growth

We are looking for a Technical Lead - GenAI with a strong foundation in Python, Data Analytics, Data Science or Data Engineering, system design, and practical experience in building and deploying Agentic Generative AI systems. The ideal candidate is passionate about solving complex problems using LLMs, understands the architecture of modern AI agent frameworks like LangChain/LangGraph, and can deliver scalable, cloud-native back-end services with a GenAI focus.

Key Responsibilities :

- Design and implement robust, scalable back-end systems for GenAI agent-based platforms.

- Work closely with AI researchers and front-end teams to integrate LLMs and agentic workflows into production services.

- Develop and maintain services using Python (FastAPI/Django/Flask), with best practices in modularity and performance.

- Leverage and extend frameworks like LangChain, LangGraph, and similar to orchestrate tool-augmented AI agents.

- Design and deploy systems in Azure Cloud, including usage of serverless functions, Kubernetes, and scalable data services.

- Build and maintain event-driven / streaming architectures using Kafka, Event Hubs, or other messaging frameworks.

- Implement inter-service communication using gRPC and REST.

- Contribute to architectural discussions, especially around distributed systems, data flow, and fault tolerance.

Required Skills & Qualifications :

- Strong hands-on back-end development experience in Python along with Data Analytics or Data Science.

- Strong track record on platforms like LeetCode or in real-world algorithmic/system problem-solving.

- Deep knowledge of at least one Python web framework (e.g., FastAPI, Flask, Django).

- Solid understanding of LangChain, LangGraph, or equivalent LLM agent orchestration tools.

- 2+ years of hands-on experience in Generative AI systems and LLM-based platforms.

- Proven experience with system architecture, distributed systems, and microservices.

- Strong familiarity with Any Cloud infrastructure and deployment practices.

- Should know about any Data Engineering or Analytics expertise (Preferred) e.g. Azure Data Factory, Snowflake, Databricks, ETL tools Talend, Informatica or Power BI, Tableau, Data modelling, Datawarehouse development.

Job Title: AI Engineer

Location: Bengaluru 

Experience: 3 Years 

Working Days: 5 Days

About the Role

We’re reimagining how enterprises interact with documents and workflows—starting with BFSI and healthcare. Our AI-first platforms are transforming credit decisioning, document intelligence, and underwriting at scale. The focus is on Intelligent Document Processing (IDP), GenAI-powered analysis, and human-in-the-loop (HITL) automation to accelerate outcomes across lending, insurance, and compliance workflows.

As an AI Engineer, you’ll be part of a high-caliber engineering team building next-gen AI systems that:

• Power robust APIs and platforms used by underwriters, credit analysts, and financial institutions.
• Build and integrate GenAI agents.
• Enable “human-in-the-loop” workflows for high-assurance decisions in real-world conditions.

Key Responsibilities

• Build and optimize ML/DL models for document understanding, classification, and summarization.
• Apply LLMs and RAG techniques for validation, search, and question-answering tasks.
• Design and maintain data pipelines for structured and unstructured inputs (PDFs, OCR text, JSON, etc.).
• Package and deploy models as REST APIs or microservices in production environments.
• Collaborate with engineering teams to integrate models into existing products and workflows.
• Continuously monitor and retrain models to ensure reliability and performance.
• Stay updated on emerging AI frameworks, architectures, and open-source tools; propose improvements to internal systems.

Required Skills & Experience

• 2–5 years of hands-on experience in AI/ML model development, fine-tuning, and building ML solutions.
• Strong Python proficiency with libraries such as NumPy, Pandas, scikit-learn, PyTorch, or TensorFlow.
• Solid understanding of transformers, embeddings, and NLP pipelines.
• Experience working with LLMs (OpenAI, Claude, Gemini, etc.) and frameworks like LangChain.
• Exposure to OCR, document parsing, and unstructured text analytics.
• Familiarity with model serving, APIs, and microservice architectures (FastAPI, Flask).
• Working knowledge of Docker, cloud environments (AWS/GCP/Azure), and CI/CD pipelines.
• Strong grasp of data preprocessing, evaluation metrics, and model validation workflows.
• Excellent problem-solving ability, structured thinking, and clean, production-ready coding practices.

About Corridor Platforms

Corridor Platforms is a leader in next-generation risk decisioning and responsible AI governance, empowering banks and lenders to build transparent, compliant, and data-driven solutions. Our platforms combine advanced analytics, real-time data integration, and GenAI to support complex financial decision workflows for regulated industries.

Role Overview

As a Backend Engineer at Corridor Platforms, you will:

• Architect, develop, and maintain backend components for our Risk Decisioning Platform.
• Build and orchestrate scalable backend services that automate, optimize, and monitor high-value credit and risk decisions in real time.
• Integrate with ORM layers – such as SQLAlchemy – and multi RDBMS solutions (Postgres, MySQL, Oracle, MSSQL, etc) to ensure data integrity, scalability, and compliance.
• Collaborate closely with Product Team, Data Scientists, QA Teams to create extensible APIs, workflow automation, and AI governance features.
• Architect workflows for privacy, auditability, versioned traceability, and role-based access control, ensuring adherence to regulatory frameworks.
• Take ownership from requirements to deployment, seeing your code deliver real impact in the lives of customers and end users.

Technical Skills

• Languages: Python 3.9+, SQL, JavaScript/TypeScript, Angular
• Frameworks: Flask, SQLAlchemy, Celery, Marshmallow, Apache Spark
• Databases: PostgreSQL, Oracle, SQL Server, Redis
• Tools: pytest, Docker, Git, Nx
• Cloud: Experience with AWS, Azure, or GCP preferred
• Monitoring: Familiarity with OpenTelemetry and logging frameworks

Why Join Us?

• Cutting-Edge Tech: Work hands-on with the latest AI, cloud-native workflows, and big data tools—all within a single compliant platform.
• End-to-End Impact: Contribute to mission-critical backend systems, from core data models to live production decision services.
• Innovation at Scale: Engineer solutions that process vast data volumes, helping financial institutions innovate safely and effectively.
• Mission-Driven: Join a passionate team advancing fair, transparent, and compliant risk decisioning at the forefront of fintech and AI governance.

What We’re Looking For

• Proficiency in Python, SQLAlchemy (or similar ORM), and SQL databases.
• Experience developing and maintaining scalable backend services, including API, data orchestration, ML workflows,  and workflow automation.
• Solid understanding of data modeling, distributed systems, and backend architecture for regulated environments.
• Curiosity and drive to work at the intersection of AI/ML, fintech, and regulatory technology.
• Experience mentoring and guiding junior developers.

Ready to build backends that shape the future of decision intelligence and responsible AI?

Apply now and become part of the innovation at Corridor Platforms!

What you’ll be doing

 Weare much more than our job descriptions, but here is where you will begin:

 As a Senior Software Engineer Data & ML You’ll Be:

 ● Architect, design, test, implement, deploy, monitor and maintain end-to-end backend

 services. You build it, you own it.

 ● Work with people from other teams and departments on a day to day basis to ensure

 efficient project execution with a focus on delivering value to our members.

 ● Regularly aligning your team’s vision and roadmap with the target architecture within your

 domain and to ensure the success of complex multi domain initiatives.

 ● Integrate already trained ML and GenAI models (preferably GCP in services.

ROLE:

 Whatyou’ll need,

 Like us, you’ll be deeply committed to delivering impactful outcomes for customers.

 What Makes You a Great Fit

 ● 5 years of proven work experience as a Backend Python Engineer

 ● Understanding of software engineering fundamentals OOPS, SOLID, etc.)

 ● Hands-on experience with Python libraries like Pandas, NumPy, Scikit-learn,

 Lang chain/LLamaIndex etc.

 ● Experience with machine learning frameworks such as PyTorch or TensorFlow, Keras, being

 proficient in Python

 ● Hands-on Experience with frameworks such as Django or FastAPI or Flask

 ● Hands-on experience with MySQL, MongoDB, Redis and BigQuery (or equivalents)

 ● Extensive experience integrating with or creating REST APIs

 ● Experience with creating and maintaining CI/CD pipelines- we use GitHub Actions.

 ● Experience with event-driven architectures like Kafka, RabbitMq or equivalents.

 ● Knowledge about:

 o LLMs

 o Vector stores/databases

 o PromptEngineering

 o Embeddings and their implementations

 ● Somehands-onexperience in implementations of the above ML/AI will be preferred

 ● Experience with GCP/AWS services.

 ● You are curious about and motivated by the future trends in data, AI/ML, analytics