Become a RF 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.

Write the firmware that makes physical devices work — the C / C++ / Rust running on microcontrollers (STM32, ESP32, NXP, TI) and SoCs (NVIDIA Jetson, Qualcomm) inside cars, drones, IoT sensors, medical devices, and consumer electronics. Day-to-day work spans bring-up of new boards from a hardware engineer's schematic, writing device drivers and HAL layers, RTOS task scheduling (FreeRTOS, Zephyr, AUTOSAR), debugging with JTAG / oscilloscopes / logic analyzers, and meeting hard real-time and power constraints. In India, the role is concentrated in the EV stack (Ola Electric, Ather, TVS, Mahindra), defense and aerospace (BEL, ISRO, DRDO, HAL, Bharat Forge), automotive Tier-1s (Bosch India, Continental, Aptiv, Tata Elxsi), consumer hardware (boAt, Noise, OnePlus India), and global semiconductor GCCs (Qualcomm, NVIDIA, Texas Instruments, Microchip).

Senior: ₹55.0L

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

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

Performance Engineers hunt latency regressions, throughput ceilings, and memory leaks in production systems — turning p99 tail spikes and GC pause dumps into shipped fixes. The job is a mix of scientific investigation and low-level systems craft: run k6 / Gatling load tests until the service buckles, capture flamegraphs with async-profiler or Linux perf, tune JVM GC flags and thread-pool sizes, rewrite hot SQL paths after reading EXPLAIN ANALYZE, and own Apdex / p99 SLO targets that the business ties to revenue. In India the role is concentrated at high-traffic product companies that simply cannot afford latency: Flipkart (Big Billion Days), Hotstar/JioCinema (IPL concurrency: 50M+ simultaneous viewers in 2023), PhonePe (UPI), Razorpay, CRED, Zepto, Walmart Global Tech Bengaluru, and Adobe India — plus FAANG GCCs (Amazon, Google, Microsoft) where perf engineering is a formal discipline. A Performance Engineer is specifically hired when a DevOps or SRE generalist cannot go deep enough; this is the role that finds the 200ms extra latency hiding in an L3 cache miss, not just the one that reads the Grafana dashboard.

Senior: N/A

Infrastructure engineers design, build, and own the compute, network, and storage substrate that everything else runs on — VPCs, bare-metal fleets, transit gateways, BGP peering, object and block storage tiers, IAM boundaries, and multi-region failover fabrics. Unlike DevOps (which automates the deploy pipeline on top of infra) or SRE (which keeps production reliable on top of infra), infra engineering is about the layer underneath: provisioning a 500-node compute fleet with Terraform and Pulumi, designing a hub-and-spoke network topology across 15 AWS accounts, or bridging a Jio on-prem data centre to GCP via Dedicated Interconnect. In India the role is most visible at Jio Platforms (running one of the world's largest hybrid infra estates), TCS Infra Services, Wipro Infrastructure Engineering, and the platform/infra pods at product unicorns like Flipkart, Razorpay, Swiggy, and CRED where a 10-person infra team may own thousands of EC2 instances and petabytes of S3 storage.

Senior: N/A