Become a Performance Engineer.

NLP Engineers build production language systems — Indic-language models, automatic speech recognition (ASR) and synthesis (TTS), document understanding for enterprise paperwork, IVR and voice-bot stacks for Indian customer support, named-entity recognition and information extraction, and the increasingly common multimodal pipelines that fuse text with vision and speech. The work blends applied research, production engineering, and dataset craft: you train and fine-tune transformer models for low-resource Indic languages, curate parallel corpora and labeled datasets, optimize inference for cost, debug failure modes that only show up in code-mixed Hindi-English speech or in handwritten Tamil documents, and own quality SLOs that mix accuracy, latency, and fairness across 22 official Indian languages. In India through 2026, NLP is one of the highest-impact applied-AI specializations because the global English-first NLP literature transfers poorly to Indic languages — concentrated demand sits at AI-native startups (Sarvam AI, Krutrim, Ola Krutrim, Yellow.ai), the public-good NLP groups at AI4Bharat (IIT-Madras) and Bhashini (Government of India), enterprise SaaS (Freshworks, Zoho ZIA, Postman, Verloop, Haptik), fintech (Razorpay, Cred, Paytm, M2P, IDfy), and the GCCs of Microsoft, Google, Adobe, and Amazon.

Power BI Developers design, build, and maintain the BI layer that turns raw enterprise data into decision-grade dashboards for finance, operations, sales, and supply-chain teams. The core loop is: connecting heterogeneous sources via Power Query (M language), modelling star schemas with fact and dimension tables, writing DAX measures and calculated columns for time-intelligence and KPI logic, publishing to Power BI Service workspaces, enforcing row-level security policies, and tuning slow reports by reducing visual-query counts and optimising DirectQuery folding. In India, this role is the Microsoft-stack alternative to Tableau development — deeply embedded in the M365-heavy enterprises: TCS, Infosys Nia practice, Wipro's Microsoft Business Applications unit, Mahindra Group, Tata Group digital, L&T Infotech (LTIMindtree), HCL, and every banking captive running Azure Synapse or Fabric. Demand spiked in 2024-2026 as Microsoft Fabric (Lakehouse, Semantic Model, Dataflows Gen2) expanded the Power BI surface area and pushed experienced developers into the ₹18-40L band.

Keep production systems fast, available, and observable for millions of users — by writing software that automates operations, runs capacity planning, designs SLOs and error budgets, and owns the on-call rotation for critical services. The role sits between software engineering and operations: you write Go / Python / Rust code, build reliability tooling, design distributed systems for resilience, run incident response, and push back on product launches that risk SLOs. In India, SRE is a premium specialization concentrated at FAANG-IN (Google SRE Bengaluru, Amazon, Microsoft IDC, Netflix India), product unicorns (Razorpay, Flipkart, Swiggy, Dream11, PhonePe, Zerodha), and the GCCs of high-traffic US firms (Uber, LinkedIn, Atlassian, GitHub, Cloudflare, Stripe). The work overlaps with DevOps but skews more toward software engineering: reliability is a product, not a process. Senior SRE pay in India sits at the very top of the technology bracket, often above equivalent SDE-3 backend roles.

Build and operate the internal developer platform — the CI/CD pipelines, Kubernetes clusters, service mesh, secrets management, observability stack, and IaC modules — that every other engineer in the company ships on. Platform engineers turn raw cloud (AWS/GCP/Azure) into a paved road: a developer pushes code, the platform takes it from commit to canary to production with logs, metrics, and rollback baked in. In India, the role is concentrated at product unicorns (Razorpay, Zerodha, CRED, PhonePe, Swiggy), GCCs of global firms (Microsoft, Atlassian, Stripe, Walmart Global Tech), and SaaS companies scaling past 200 engineers — typically the point at which a dedicated platform team starts paying for itself in shipping velocity.

Solutions Architects are the customer-facing technical role that bridges what a product can do and what a customer actually needs. They design end-to-end deployments, integrations, and migrations on behalf of the customer's engineering team — sizing infrastructure, mapping data flows, picking the right product modules, drafting reference architectures, and partnering with sales and customer-success to win and expand accounts. The role is genuinely hybrid: it requires deep technical depth (cloud, networking, security, distributed systems) and high verbal craft (workshops, executive presentations, written design docs that survive procurement and security review). In India through 2026, Solutions Architect is one of the highest-paid customer-facing technical roles, concentrated at the GCCs of cloud vendors (AWS India, Microsoft Azure India, Google Cloud India, Oracle, IBM), enterprise SaaS companies (Salesforce India, ServiceNow, Snowflake, Databricks, MongoDB, Confluent), B2B Indian product companies (Freshworks, Postman, Atlan, Hasura, Chargebee), and the systems-integrator giants (TCS, Infosys, Wipro, Accenture) where the role sits closer to delivery. Top-tier Solutions Architects in India routinely cross ₹1Cr total comp by L6+ and the role is a common path into VP-Engineering and Field-CTO seats.

Prompt Engineers design, evaluate, and ship LLM-powered features — system prompts, RAG flows, agent orchestration, structured-output schemas, and the eval harnesses that prove a prompt is actually better. The role sits between product, applied ML, and software engineering: you write prompts the way other engineers write code, run cost-quality-latency trade-off experiments, instrument grader pipelines, and own the part of the product that the LLM actually 'speaks.' In India through 2026, the role is one of the fastest-growing AI hires — concentrated at AI-native startups (Sarvam AI, Krutrim, Ola Krutrim, Atlan, Yellow.ai), product SaaS shops with a serious AI feature surface (Freshworks, Postman, Chargebee, Whatfix, Zoho ZIA), fintechs (Razorpay, Cred, Paytm), and the GCCs of Microsoft, Google, Adobe, and Salesforce. The salary band is unusually wide because the title is new and JDs vary from 'wrote one ChatGPT integration' to 'owns the eval harness for a frontier model.' Sarvam AI made several public crore-level offers to senior prompt and LLM engineers in 2025.

ISRO Scientists and Engineers (Scientist/Engineer 'SC' entry, Group A Gazetted) are selected through the ISRO Centralised Recruitment Board (ICRB) exam in disciplines spanning Mechanical, Electronics, Computer Science, Civil, and Architecture. Posting centres span VSSC Thiruvananthapuram (cryogenic and liquid propulsion, launch vehicle systems), SDSC SHAR Sriharikota (launch operations, range safety), URSC Bengaluru (satellites — communication, navigation, Earth observation), SAC Ahmedabad (payload technology, microwave remote sensing, data processing), and NRSC Hyderabad (geodesy, remote sensing applications, geospatial analytics). Entry at 7th Pay Commission Level 10 (₹56,100–1,77,500 basic) includes HRA, TA, medical, LTC, and subsidised accommodation; the ladder runs to Scientist 'SD/SE/SF/SG/H/G', Outstanding Scientist, and Distinguished Scientist — the cadre that led Chandrayaan-3 to the lunar south pole in August 2023.

Senior: ₹31.0L

Cloud Security Engineers secure AWS, Azure, and GCP environments at Razorpay, Cred, Swiggy, Flipkart, FAANG India captives, and Big-4 cloud audit practices. The role combines IAM least-privilege design, CSPM tooling (Wiz, Prisma Cloud, Lacework), container and Kubernetes security, CI/CD pipeline hardening, and cloud-native incident response — not general SOC work adapted to cloud, but security designed natively around the Shared Responsibility Model. Entry demands at minimum one cloud security certification (AWS Security Specialty, Azure SC-100, or CCSP) and hands-on experience with at least one major CSPM/CNAPP platform.

Senior: N/A

ML Infrastructure Engineers build and operate the GPU clusters, distributed training platforms, and inference serving stacks that ML and AI teams run on. The role sits between ML Engineering (which authors models) and DevOps (which manages general compute): an ML Infra Engineer owns multi-node NCCL training coordination, Kubernetes and Ray/Slurm job scheduling for GPU workloads, DCGM-based GPU health monitoring, distributed checkpointing, and production inference serving via vLLM, NVIDIA Triton, or TGI. In India through 2026, the role is growing fastest at Sarvam AI and Krutrim (building in-house GPU training infrastructure for Indic foundation models), Microsoft India and Google India (scaling GCC ML platform teams), Adobe India (GPU inference for Firefly), and hyperscaler-adjacent startups — Fractal Analytics and Mu Sigma running large-scale GPU fleets for enterprise ML. FAANG-India ML platform teams in Hyderabad and Bengaluru represent the top of the India-based compensation range. Entry paths are typically from software engineering or HPC backgrounds with strong CUDA and Kubernetes experience; pure data-science backgrounds rarely transition directly.

Senior: N/A

Kubernetes Engineers design, operate, and evolve the container orchestration layer that powers production workloads at scale — writing Helm charts and Kustomize overlays, authoring Custom Resource Definitions and Kubernetes Operators, tuning scheduler behaviour with taints, tolerations, and node affinity rules, and running GitOps workflows through ArgoCD or Flux. In India, the role is concentrated at product unicorns (Razorpay, PhonePe, Swiggy, Zomato, Flipkart, CRED), CNCF-adjacent software vendors with India presence (Red Hat, VMware Tanzu, Rafay Systems), and GCCs of global firms (Google, Microsoft, Atlassian, Walmart Global Tech, Goldman Sachs) who run large multi-cluster Kubernetes fleets on EKS, GKE, or AKS. A Kubernetes Engineer is the specialist version of a DevOps/Platform Engineer — every tool, every debug session, and every design decision revolves specifically around the Kubernetes control plane, workload API, and cloud-native ecosystem.

Senior: N/A

Snowflake Engineers design, build, and govern production data platforms on Snowflake — sizing virtual warehouses, tuning credit consumption, architecting Snowpipe and Snowflake Streaming ingestion flows, building dbt-on-Snowflake transformation layers, and operating Snowflake's RBAC + row-access policies to meet compliance requirements. Unlike a generic Data Engineer who treats Snowflake as one option among several, a Snowflake Engineer goes deep on the platform itself: micro-partition pruning, clustering keys, dynamic tables, Snowpark Python UDFs, Snowflake Cortex ML functions, replication groups for HA, and the credit economics that determine whether a warehouse costs ₹2L or ₹20L per month. In India, this specialisation commands a 20-35% premium over generic data engineering because the Snowflake billing model punishes misuse harshly — companies running Razorpay, PhonePe, Walmart Labs India, Cred, and Zepto on Snowflake pay real money for every compute-second and need engineers who can cut that cost while maintaining SLOs.

Senior: N/A