Head of Technology

Solid product experience and an educational background from Tier‑1 universities are preferred.

As a Senior AI Engineer, you will play a key role in designing and delivering highly scalable, next generation AI products within our AI stack. You will work in a technically rich environment with talented peers, contributing to product planning, design, and execution while influencing the evolution of Teradata’s AI capabilities. 

In this role, you will: 

• Design, develop, and maintain high quality systems, services, and applications with an emphasis on scalability, reliability, and performance. 
• Collaborate with cross-functional engineers and product partners to shape design and consistently deliver end to end features. 
• Build and integrate robust RESTful APIs, ensuring security, data consistency, and maintainability. 
• Work with SQL and NoSQL databases to implement efficient data models and service access patterns. 
• Apply and experiment with AI/ML technologies, including agentic AI and large language models (LLMs). 
• Use AI powered engineering tools to improve development quality, speed, and productivity. 
• Mentor engineers, supporting them in technical planning, implementation, and best practices. 
• Identify and resolve system performance bottlenecks, optimizing code, design, and infrastructure. 
• Write unit and integration tests and participate in code reviews to uphold engineering excellence. 
• Investigate production issues, ensuring timely and effective solutions. 
• Stay current with emerging trends in engineering, AI/ML, and cloud-native technologies. 

Who You’ll Work With 

You will be part of a high-performing engineering team building core components of Teradata’s AI platform. You will collaborate closely with other engineers, AI/ML specialists, product managers, cloud architects, and partner teams across the company. The role offers opportunities to contribute directly to Teradata’s AI product roadmap and technological direction. 

What Makes You a Qualified Candidate 

(Nonnegotiable qualifications) 

• Bachelor’s degree in Computer Science or equivalent from a recognized institution. 
• 5+ years of experience in backend services, distributed systems, or data platform development. 
• Strong proficiency in Java, Go, or Python for service development. 
• Good understanding of design principles, distributed system patterns, and service framework. 
• Hands-on experience designing and developing RESTful APIs. 
• Experience with SQL and NoSQL databases and data modelling. 
• Strong debugging, problem solving, and troubleshooting skills. 
• Experience with modern containerization and orchestration tools such as Kubernetes. 
• Knowledge of public cloud platforms. 
• Experience with AI productivity tools (e.g., GitHub Copilot). 
• Ability to work independently, drive workstreams, and deliver high quality outcomes. 
• Strong communication and collaboration skills. 

What You’ll Bring 

(Preferred qualifications) 

• Familiarity with messaging systems such as Kafka. 
• Experience experimenting with or implementing AI/ML technologies, Agentic AI, or LLMs. 
• A track record of influencing design decisions and driving engineering best practices. 
• The ability to mentor engineers and guide them through design, planning, and execution. 
• A proactive mindset and comfort working in fast paced, dynamic environments. 
• Curiosity, adaptability, and a passion for building impactful AI-driven products. 

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

• Master’s degree or PhD in Computer Science or a related field with a focus on Artificial Intelligence (AI) or Machine Learning (ML).
• 7 years of software experience designing and deploying large-scale AI/ML applications and complex models.
• Experience with MLOps, establishing best practices for production model monitoring, maintenance, and lifecycle management.
• Experience leading and mentoring engineers, and translating complex business objectives into scalable AI-driven roadmaps.
• Experience architecting scalable data pipelines using BigQuery, Beam, or Dataflow, and deploying models on Google Cloud Platform.
• Understanding of Python and frameworks like TensorFlow, JAX, and Scikit-learn, with deep statistical modeling knowledge.

About the job

The People Operations Engineering team is dedicated to building innovative and scalable technology solutions that empower a global workforce. Leveraging the power of data, AI, and machine learning, the team optimizes HR processes, enhances employee experiences, and drives organizational efficiency. Joining this team offers the opportunity to lead the application of AI to solve complex, real-world business challenges.

We are seeking a highly experienced and results-oriented Senior AI Software Engineer to join the Human Resources Engineering (HRE) team in Hyderabad.

In this role, you will serve as a technical leader, driving the design, development, and deployment of sophisticated AI-powered solutions that significantly impact employees across the organization. You will work on large-scale infrastructure, mentor a talented team of engineers, collaborate with cross-functional stakeholders, and help shape the future of HR through innovative AI applications. You will be responsible for translating complex business challenges and strategic objectives into scalable, efficient AI/ML systems.

As part of the Corporate Engineering organization, you will build world-class business solutions that support a global enterprise. The team delivers end-to-end platforms, tools, and experiences that enable employees to work more effectively and drive innovation across the organization.

Responsibilities

• Lead the technical design and architecture of complex, scalable AI/ML systems and infrastructure within the HR engineering domain, ensuring high reliability, performance, and long-term sustainability.
• Drive the development of advanced AI/ML algorithms for HR applications such as talent acquisition and employee engagement using Python and frameworks like TensorFlow and JAX.
• Architect large-scale data pipelines for data ingestion, cleaning, and feature engineering using modern data processing frameworks (e.g., Beam, Dataflow), while leading root cause analysis and system troubleshooting.
• Own the end-to-end MLOps lifecycle, including model deployment, monitoring, optimization, and maintenance, while establishing best practices for software development and code quality.
• Collaborate with cross-functional stakeholders to translate business needs into scalable AI/ML solutions, mentor engineers, and drive the adoption of emerging AI technologies across the organization.

About the role

You will be a pivotal member of the leadership team, responsible for driving the company's product and platform strategy while overseeing engineering excellence across AI, data, platform, DevOps and infrastructure. This is a mission-critical role for an engineering leader who is both strategic and hands-on, and who can build scalable technologies for global enterprise clients in a fast-paced innovation ecosystem.

Reports to: CEO · Location: Bengaluru, India — hybrid, 3 days a week in office

What you will do

Technology & product leadership

• Define and drive the technology vision, architecture and long-term platform roadmap.
• Oversee the architecture, design and delivery of highly scalable enterprise systems.
• Ensure engineering excellence, velocity and reliability across the product lifecycle.

Engineering & platform management

• Lead platform engineering, product technology, DevOps, infrastructure and Quality Engineering.
• Build robust cloud-native systems using the Azure, AWS and GCP ecosystems.
• Oversee operational effectiveness, including uptime, production reliability and cost optimisation.

Innovation & AI strategy

• Spearhead Mission AI by developing scalable, production-grade AI/ML and GenAI capabilities.
• Own the GenAI/LLM solutions architecture.
• Core specialisation in AI agents and autonomous workflows, data extraction and intelligent automation.
• Direct hands-on architectural oversight of large language models, applied AI and multi-layered deep learning products.
• Extensive expertise in building intelligent document automation systems — similar in complexity to patent parsing, legal workflow automation and structured decision intelligence tools.

Technical leadership

• A proven track record overseeing product architecture, tech strategy and cross-functional engineering execution across core full-stack and AI platforms.
• Collaborate with executive leadership on business strategy, client requirements and product delivery.
• Build, mentor and scale high-performing engineering teams with a growth mindset.
• Establish a strong technology culture grounded in ownership, innovation and continuous learning.

What success looks like

• Mission AI: build a world-class AI system leveraging GenAI, ML and enterprise-grade data engineering.
• Hyper-scaling: architect and scale the platform to match global industry leaders in the IP space.
• Culture building: develop a strong engineering organisation with high ownership, performance and innovation DNA.

Qualifications & experience

• Preferably under 15 years of enterprise software engineering experience across B2B SaaS or technology-first companies; fewer is fine for an exceptional engineer.
• A proven track record of taking early-stage AI/ML prototypes and scaling them into robust enterprise SaaS platforms featuring multi-agent orchestration, complex workflows and decision intelligence.
• Experienced in building and mentoring agile, lean startup teams of full-stack and machine learning engineers from the ground up.
• Proven leadership in defining and executing technology strategy and platform roadmaps.
• Extensive cloud-native engineering experience with Azure, AWS and GCP.

Technical expertise

• Strong full-stack engineering background (Java, Python, JavaScript frameworks).
• Expertise with JS frameworks such as React, Angular and Node.js.
• Experience building and scaling distributed systems and microservices.
• Strong knowledge of databases (SQL, NoSQL), data modelling and unstructured data management.
• Strong understanding of Agile methodologies and tools (Atlassian, Git, CI/CD pipelines).

Behavioural & leadership competencies

• Product and delivery management expertise, end to end, including delivery and customer support.
• Excellent communication, with the ability to influence executive stakeholders.
• High technical proficiency combined with strong business acumen.
• Strong analytical and decision-making skills.

Senior Technical Manager

Primary objective: Own engineering teams, architecture, delivery governance and successful execution of product and AI initiatives across DocTutorials.

Location

Hyderabad / Chennai

Employment

Full-time

Experience

10+ years

Role Summary

The Senior Technical Manager combines technical leadership, people management, product delivery and project execution. The role is accountable for team outcomes, engineering quality, timelines, production stability and the disciplined delivery of AI projects.

Key Responsibilities

1. Technology and Architecture Leadership

·       Provide direction for Python FastAPI, React/Next.js, data, cloud and AI architectures.

·       Review solution designs for security, scalability, maintainability, performance and cost.

·       Define engineering standards and resolve complex cross-team technical risks.

·       Remain technically hands-on through architecture reviews, critical code reviews and incident debugging.

2. People and Delivery Management

·       Lead, mentor and develop engineering teams with clear goals and accountability.

·       Own estimation, capacity, execution, dependencies, risks, releases and delivery recovery plans.

·       Conduct regular one-to-ones, performance reviews, coaching and skill-development planning.

·       Work with Product, QA, DevOps and stakeholders to deliver measurable product outcomes.

3. AI Project Execution

·       Assess AI use cases for feasibility, data readiness, risk, cost and measurable value.

·       Lead delivery of LLM, RAG, agentic and AI-assisted product initiatives from PoC to production.

·       Ensure evaluation, human validation, security, privacy, observability and rollback controls.

·       Prevent isolated demos, uncontrolled AI usage and dependency on individual engineers or vendors.

Required Skills and Experience

·       10+ years in software engineering with significant technical and people leadership experience.

·       Strong understanding of Python/FastAPI, React/Next.js, APIs, databases, cloud and distributed systems.

·       Proven ownership of product delivery, project planning, risk management and production operations.

·       Experience hiring, mentoring, evaluating and developing engineering teams.

·       Strong knowledge and execution experience across AI/ML, LLM, RAG or agentic-AI projects.

·       Practical understanding of Generative AI, LLMs, RAG, Agentic AI and responsible AI use relevant to the role.

·       Ability to use approved AI tools productively while protecting source code, credentials, learner data and organisational information.

·       Commitment to human review, testing, security validation and traceability for AI-assisted outputs.

Good-to-Have Experience

·       EdTech or medical-learning product experience.

·       AWS, Kubernetes, DevSecOps, MLOps or LLMOps.

·       Experience scaling teams and platforms through major transformation.

How Success Will Be Measured

·       Delivery becomes predictable with risks identified early.

·       Engineering quality, security and production stability improve.

·       AI projects progress from validated use cases to reliable production outcomes.

·       Team capability, ownership, engagement and succession strength increase.

·       Work consistently delivers measurable outcomes and business value—not merely completion of assigned tasks or activities.

Expected mindset: Own the outcome, use AI responsibly, communicate risks early and deliver secure, measurable value for DocTutorials and its learners.

Job Details

Position Title: AI Evangelist

Role Purpose

We are seeking a highly experienced AI Evangelist & Core AI Engineer who combines deep technical expertise in Artificial Intelligence with the ability to influence, educate, demonstrate, and accelerate AI adoption across the enterprise.

The role requires deep understanding of AI, Machine Learning, Deep Learning, Generative AI, Large Language Models, Agentic AI, AI engineering, and modern software development practices, combined with significant hands-on experience implementing scalable AI applications in complex enterprise environments.

Key Responsibilities

1. Core AI Engineering & Architecture

• Design, develop, and implement enterprise-grade AI and Generative AI applications.
• Build scalable applications using LLMs, multimodal models, AI agents, RAG, vector databases, knowledge graphs, APIs, and enterprise data platforms.
• Architect AI solutions capable of supporting enterprise-scale workloads with appropriate considerations for:
• Scalability
• Reliability
• Security
• Performance
• Observability
• Cost optimization
• Governance
• Develop and implement advanced Retrieval-Augmented Generation (RAG) architectures including hybrid search, reranking, metadata filtering, semantic retrieval, and knowledge grounding.
• Build multi-agent and agentic AI applications capable of reasoning, tool usage, workflow execution, orchestration, and enterprise system integration.
• Design AI applications integrating structured and unstructured enterprise information.
• Evaluate architecture choices between commercial models, open-source models, small language models, domain-specific models, and hosted AI platforms.
• Implement model routing, prompt orchestration, caching, guardrails, evaluation pipelines, and fallback strategies.
• Work closely with cloud, platform, security, enterprise architecture, DevOps, and application engineering teams.

2. Deep AI / ML / DL Expertise

The candidate should have strong conceptual and practical understanding across the modern AI technology landscape, including:

• Artificial Intelligence /Machine Learning/ Deep Learning/ Neural Networks /LLM/SLM/RAG/Graph RAG / MLOps / LLMOps / GenAIOps

3. Generative AI & LLM Engineering

• Build production-grade applications utilizing leading commercial and open-source LLM ecosystems.
• Design scalable LLM platforms supporting multiple enterprise applications and use cases.
• Develop reusable LLM services, APIs, components, agent frameworks, and reference architectures.

4. AI Agents & Automation

• Design and implement AI agents and agentic workflows that automate complex business and engineering processes.
• Build agents capable of interacting with enterprise applications, APIs, databases, documents, development environments, and workflow platforms.
• Evaluate emerging agent architectures and orchestration frameworks pragmatically.
• Develop reusable patterns for:
• Single-agent systems
• Multi-agent systems
• Human-in-the-loop workflows
• Autonomous workflows
• Tool-calling agents
• Coding agents
• Enterprise knowledge agents
• Establish appropriate controls for AI agent identity, permissions, auditability, security, and human oversight.

5. AI-Enabled Software Engineering

Drive adoption of AI throughout the Software Development Life Cycle.

Java Lead – TOP MNC

📍 Location: Bangalore Only (No Relocation)

💼 Experience: 12–15 Years

✅ Mandatory Skills:

• Strong Java Development experience

• Cucumber – 6+ months

• AI – 1+ year

• Technical Leadership

⚠️ Important: Candidates must be from a Java Development background. Pure Testing/QA profiles are not eligible.

⏳ Immediate Joiners Only – Must be able to join before November 25, 2026.

Director of Engineering (AI Solutions & Delivery)

About the Role

We are looking for a Director of Engineering to lead engineering execution and delivery across our enterprise AI solutions. This role combines strong engineering leadership with operational discipline, ensuring AI products move from technical design to production with predictable timelines, high quality, and reliable performance.

You will lead Engineering Managers and Tech Leads, oversee multiple engineering teams, and drive delivery excellence across AI-powered applications and platforms. The focus is on execution, technical quality, and organizational accountability, ensuring teams deliver scalable, production-ready AI systems without compromising reliability or engineering standards.

Key Responsibilities

1. Engineering Delivery & Execution

• Own engineering delivery schedules, translating product roadmaps into achievable sprint plans and release milestones.
• Establish measurable delivery targets, aiming for at least 85% on-time delivery across engineering teams.
• Identify and resolve cross-team dependencies, technical bottlenecks, and resource constraints before they affect delivery.
• Drive engineering process improvements across Agile development, CI/CD, automated testing, and release management.
• Monitor delivery metrics, sprint commitments, and release readiness to maintain predictable execution.

2. AI Quality, Evaluation & Governance

• Establish automated AI evaluation pipelines to measure hallucinations, relevance, groundedness, and contextual accuracy before production releases.
• Define quality benchmarks and evaluation criteria for LLM-powered applications and AI-driven workflows.
• Implement safety mechanisms, validation filters, circuit breakers, and human-in-the-loop fallbacks to manage unpredictable AI outputs.
• Oversee RAG pipelines, vector databases, data ingestion, and retrieval mechanisms to maintain accurate, reliable, and compliant context.
• Reduce production AI quality regressions through continuous evaluation, monitoring, and feedback loops.

3. Architecture, Scalability & Reliability

• Oversee the architecture of distributed systems and cloud-native applications to support scalability, availability, and performance.
• Drive reliability targets, including 99.9% uptime and low time-to-first-token (TTFT) latency for applicable AI applications.
• Partner with Staff and Principal Engineers to design resilient systems, including fallback mechanisms for model API failures, timeouts, and rate limits.
• Establish engineering standards for code reviews, observability, automated integration testing, and security compliance.
• Ensure production systems meet defined service-level objectives (SLOs) and performance benchmarks.

4. Engineering Leadership & Team Development

• Lead and develop Engineering Managers and Tech Leads, establishing clear ownership, accountability, and delivery expectations.
• Build a high-performance engineering culture focused on technical excellence, collaboration, and continuous improvement.
• Partner with Talent Acquisition to attract, assess, and hire strong software engineering and AI talent.
• Align engineering resources with business priorities, delivery commitments, and technical requirements.
• Address performance and execution challenges while maintaining team engagement and engineering quality.

Success Metrics: First 90 Days

First 30 Days

• Assess existing engineering workflows, delivery pipelines, and release processes.
• Establish baseline AI evaluation criteria and identify gaps in production quality.
• Take ownership of engineering delivery across teams and align expectations with Engineering Managers and Tech Leads.

First 60 Days

• Implement automated AI evaluation frameworks and establish measurable quality benchmarks.
• Improve cross-team coordination and resolve key delivery dependencies.
• Establish consistent sprint planning, release cadences, and delivery reporting.

First 90 Days

• Achieve at least 85% sprint commitment completion.
• Demonstrate measurable reductions in production AI quality regressions.
• Improve system reliability and stabilize latency against defined performance targets.
• Establish clear accountability and predictable delivery practices across engineering teams.

Qualifications & Requirements

• Experience: 14+ years in software engineering, including at least 5 years managing Engineering Managers or Tech Leads in enterprise or high-growth environments.
• AI/ML Expertise: Hands-on experience delivering AI-powered systems involving LLMs, RAG architectures, vector databases, prompt engineering, or model fine-tuning workflows.
• Engineering Delivery: Proven ability to lead multiple engineering teams and deliver complex technical initiatives from design through production launch.
• System Architecture: Experience with distributed systems, cloud-native backend infrastructure, and scalable APIs across AWS, GCP, or Azure.
• AI Quality & Reliability: Understanding of AI evaluation frameworks, production monitoring, hallucination mitigation, guardrails, and system reliability.
• Delivery Management: Strong experience with Agile methodologies, CI/CD, release planning, and engineering performance metrics.
• Leadership: Ability to establish accountability, manage competing priorities, and deliver against ambitious deadlines without compromising quality or team morale.

What Success Looks Like

The successful candidate will establish predictable engineering delivery, improve the quality and reliability of production AI systems, and build a culture of ownership across engineering teams. They will combine technical judgment with execution-focused leadership to ensure AI solutions are delivered on schedule, operate reliably, and meet enterprise expectations.

If you are passionate about building high-performing engineering teams and delivering enterprise-grade AI solutions at scale, we would like to hear from you.

AI Solution Architect

Experience: 12+ Years

Location: Coimbatore / Hybrid

Joining: Immediate / Within 30 Days

Primary Skills: Enterprise Architecture, Python, REST APIs, Microservices, Distributed Systems, Cloud (AWS/Azure/GCP), CI/CD, Docker, Kubernetes, Enterprise Data Architecture, Integration Patterns.

Preferred Skills: LLM Integrations, RAG Pipelines, AI Agents, Agentic Workflows, MCP (Model Context Protocol), Vector Databases, Prompt Engineering, LangChain/LlamaIndex, LLMOps, Multi-Agent Orchestration.

About the Role

At Techjays, we're helping companies reimagine how they build, operate, and scale with AI at the core. We focus on building secure, scalable, production-grade AI systems that solve real business problems and deliver measurable outcomes.

We're looking for an AI Solution Architect who can own enterprise-level solution architecture end-to-end. The ideal candidate combines deep technical expertise with the ability to understand complex business challenges, engage with prospective clients, and design compelling, scalable, AI-native solutions.

This is a senior technical role at the intersection of enterprise architecture, pre-sales, solutioning, and delivery governance. You'll collaborate with leadership, engineering teams, and clients to architect solutions spanning AI integrations, RAG pipelines, agentic workflows, cloud infrastructure, and enterprise platforms.

This role requires a strong technical background and current hands-on expertise. Candidates with purely pre-sales experience or non-technical solutioning backgrounds will not be a fit.

Key Responsibilities

• Own end-to-end enterprise architecture for AI-native platforms, defining high-level and low-level designs across backend systems, data platforms, and AI components.
• Lead solutioning for complex enterprise engagements, translating client requirements into technically sound, secure, scalable, and commercially viable architectures.
• Define reference architectures and design standards for LLM integrations, RAG pipelines, agentic workflows, MCP-based integrations, and enterprise data platforms.
• Partner with pre-sales and business development teams to prepare RFP responses, technical proposals, solution presentations, and architecture documents.
• Lead client discovery sessions and technical presentations, communicating architectural decisions and trade-offs to technical and executive stakeholders.
• Conduct architecture reviews and technical assessments to ensure scalability

About the Company

As the world moves toward AI-native applications, one element matters more than anything: data.

We are building a platform that helps organizations unlock the value hidden within customer feedback. By centralizing data from multiple sources—including CRM platforms, support systems, collaboration tools, surveys, community forums, and customer conversations—into a unified intelligence layer, we enable teams to understand customer needs and make better product decisions.

Built on top of this foundation is an AI-powered platform that allows teams to query, monitor, and act on customer intelligence directly within their workflows. Our customers include some of the fastest-growing product-led technology companies as well as large global enterprises.

Backed by leading global venture capital firms, our engineering culture is AI-native from the ground up. Agents, LLMs, and applied machine learning are not side projects—they are central to how we build products. We operate at the cutting edge of agentic systems and continuously push the boundaries of what modern AI applications can achieve.

What You Will Do

• Convert product direction into phased technical plans, including estimates, milestones, dependencies, and risk assessments.
• Make thoughtful trade-offs across correctness, latency, user experience, AI/model behavior, reliability, cost, and delivery speed.
• Define clear ownership boundaries across frontend applications, BFF layers, backend services, agent workflows, ML platforms, and data contracts.
• Review ERDs, PRDs, design documents, and implementation plans to identify gaps before they become execution challenges.
• Drive planning and execution for complex initiatives while empowering area leads and senior engineers to maintain ownership.
• Partner closely with Product, Design, Engineering, and GTM teams to turn ambiguous problem statements into executable solutions.
• Influence technical direction through strong engineering judgment, architectural leadership, and hands-on involvement.
• Mentor senior engineers and contribute to engineering excellence through reviews, incident analysis, and proactive risk management.

What It Takes

• 9+ years of experience as a Senior Engineer, Technical Lead, or equivalent role.
• Demonstrated experience owning architecture and execution for large-scale product or platform initiatives spanning multiple teams.
• Strong foundation in distributed systems and modern software architecture.
• Exposure to agentic AI systems, machine learning engineering, MLOps platforms, and frontend/BFF architectures.
• Ability to own end-to-end product and technical problems rather than specializing exclusively in a single layer of the stack.
• Proven track record of converting ambiguity into actionable engineering plans.
• Excellent written communication skills, including design documentation, architectural reviews, trade-off analyses, and RCA documentation.
• Ability to challenge requirements constructively and collaborate effectively across Product, Design, Engineering, and GTM functions.
• High ownership mindset, attention to detail, and a low-ego, collaborative approach.

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)